Digital Filter Design Methods for Hearing Aid Performance Improvement

Digital Filter Design Methods for Hearing Aid Performance Improvement | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Systematic Review Digital Filter Design Methods for Hearing Aid Performance Improvement MUNYARADZI CHARLES RUSHAMBWA, Rachael Samkange, Peter Musiiwa, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8200058/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract In recent years, many people have been affected with hearing problems, yet the effect is not significant on human lives. This is where a hearing aid comes into rescue where signals are processed such that the human audiogram is able to pick them. These electronic devices should have a specific design characteristic since they are wearable. Design of these portable devices requires less hardware, low delay and low power use. These characteristics highly depend on the makeup of the hearing aid heart which is the filter bank. This paper reviews the different design techniques for a low delay and energy efficient decimation filter for se in hearing aids for performance analysis. Biomedical Engineering audio digital signal processing filter bank hearing aid speech signal Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. INTRODUCTION World Health Organization gave a report outlining that (WHO, 2025 ), In the whole world, around 5% of the world’s population is affected by hearing impairments. According to (Yong Lian and Ying Wei, 2005 ), a person will not pick sound signals that fall below a minimum level also known as hearing threshold. A hearing impaired person cannot hear well as others with hearing threshold of twenty decibels or better in both ears (WHO, 2024 ). Hearing impairment is not a one size fits all type of situation, it is in different forms (Pandey sidheshwar, Fayez Bahmad Jr, 2015 ). Three main types are sensoneurial, conductive and hybrid hearing loss (Pandey sidheshwar, Fayez Bahmad Jr, 2015 ). The Fig. 1 gives an illustration of the sages of hearing loss and the hearing threshold in decibels. For mild hearing loss one will be facing difficulty in understanding normal speech. For hearing threshold of forty to seventy decibels there will be difficulty in understanding loud speech Severe hearing loss occurs when a person can only understand amplified speech. Hearing thresholds above ninety decibels present challenges in comprehending amplified sounds, which indicates profound hearing loss. Hearing impairment can affect one or both ears. Patients face increasing hearing thresholds, limited frequency range, reduced sound quality, and restricted sensitivity. Hearing aid devices can greatly help patients with mild to profound hearing loss communicate better. These electronic devices compensate for the weakening ability of the human ear, improving the quality of sound signals for patients. Clear speech becomes much easier to hear when sound signals are transmitted at suitable volumes. Hearing aids are designed to amplify and process audio signals to match a person’s hearing profile, helping to address the difficulties caused by hearing loss. Research by the World Intellectual Property Organization (WIPO) highlights efforts to develop innovative solutions for individuals with hearing loss, as shown in Fig. 2 (Solomon & Pardeep Bhandari). The major causes of hearing loss include genetics, exposure to toxic substances, excessive noise, and aging. The World Health Organization predicts that by 2050, around seven hundred million people may experience hearing loss. Many children with severe hearing impairment struggle to access education in developing countries. Hearing aids can provide significant assistance to those who are deaf, but they are often only available to individuals in industrialized nations. If untreated, hearing loss may lead to Alzheimer’s disease or dementia, as reported by Johns Hopkins, indicating a connection between these conditions (Hopkins, 2021 ). Advancements in digital signal processing are making modern hearing aids more versatile, smaller, and more widely used. These devices can amplify audio signals based on the user's specific hearing needs, acting as a delicate aid for those with hearing impairment. The core of this development lies in the design, which influences how sound is processed and delivered. Filters in hearing aids perform various functions, such as enhancing speech clarity, reducing background noise, and fine-tuning frequency responses to meet the user’s hearing profile. The design of these filters must consider power efficiency, latency, performance, and size. If the delay exceeds twenty milliseconds, it can hinder lip reading and diminish user satisfaction with the hearing aid (Justin R. Burwinkel, Au.D.). Analogue and digital hearing aids are two types available. Analogue hearing aids change the input signal into electrical signals whereas digital hearing aids change the input audio signal into binary numerical information (S V V Satyanarayana, 2023 ). Digital hearing aids are more preferred over analogue because they are adaptable and self-adjusting. There are two basic types of digital filters which are used in hearing aid design. Infinite impulse response (IIR) are more reliable in terms of memory an computational complexity however can bring about distortion problem if not designed with care (risla fathima p, pranav kumar m p, 2019). Finite Impulse Response (FIR) filter is mostly chosen because of it being stable, sensible to coefficients and linear phase property (Z. Shang, Y.Lian and Y. Zhao, 2017). The massive increase in the number of individuals with hearing loss and difficult situations they meet pin points a case of emergency to advance and improve hearing aid technology. Out there those who should be having hearing aids are moving without them because the existing hearing devices possess some characteristics and limitations. n of digital filter design a very important the Engineers and researchers have taken studies on filter design methods each making a significant input in improving performance of the hearing aid. This paper gives an overview of some challenges faced by hearing aid users, such as battery consumption and throughput. The filter bank often being essential for enhancing digital hearing aids, requires careful attention to improve performance. This study is concentrated on analyzing hearing aid filter designing methods. Human natural audiogram have a hearing range of twenty hertz to twenty kilohertz, however the ear is more responsive in the four kilohertz band. 2. REVIEW METHODOLOGY The significant rise in the number of individuals with hearing loss, along with the challenges they face, underscores the urgent need to develop and improve hearing aid technology. Many people who could benefit from hearing aids do not have access to them because current devices still have limitations. Researchers and engineers have studied filter design methods to enhance hearing aid performance. Resources such as Research4life, Google Scholar, Science Direct, and other online databases were used to find articles and materials for analysis. The key search terms focused on digital filter types and their design techniques to ensure the hearing aid filter met necessary characteristics. All design methods were validated and compared. The study excluded research published before 2010 and any articles that hadn't undergone peer review. From the search, two hundred papers were identified. Fifty duplicates were removed during the initial screening. After evaluating the relevance of abstracts, ninety-five of the remaining one hundred and fifty articles were eliminated. Consequently, this paper is based on thirty journal papers. The evaluation of design techniques focused on characteristics offered by digital filters when processing audio signals. 3. METHODS OF DESIGNING THE FILTER BANK AND THE HEARING AID SYSTEM 3.1 Part Played by Hearing Aid in Reducing Hearing Impairment Sensorineural Hearing Loss (SNHL) is the most common type of hearing loss. In this condition, tiny hair cells in the cochlea of the inner ear can no longer effectively detect certain sound frequencies. Typically, individuals with sensorineural hearing loss find it difficult to hear clearly in noisy surroundings. Hearing aids help by amplifying specific sound signals that correspond to human hearing abilities. The below figure illustrates the hearing aid system, including amplification, conversion, filtering, and output of audio signals. A hearing aid is designed to capture audible signals using a microphone. The microphone converts weak audio signals into stronger ones. These amplified signals are then sent to the ear through a speaker. Digital hearing aids enhance interaction, making communication clearer and more comfortable. Signal processing algorithms serve to improve speech sounds, which are essential in daily life. A hearing aid consists of an amplifier that strengthens faint sounds so they become loud and clear. The filter bank is the heart of the hearing aid, filtering out background noise to emphasize speech. The hearing aid processes audio signals by adjusting amplitude, shifting frequencies, and applying compression for a smoother and more natural output (Prerna Kumari, Deepak Singh, 2021). A crucial feature of hearing aids is noise cancellation, which enables users to focus on important sounds. Engineers face several challenges in designing hearing aids, including the need for small integrated circuits, low power consumption, improvements in sound balance, and managing high peak power demands as wireless features become more common. They must also prevent frequency overlap, minimize background noise in sensitive components like filters, maintain a narrow transition width, and reduce the number of logical operations. 3.2 Hearing Aid Filter Bank The challenges researchers face highlights the need for hearing devices to be tailored to meet individual users’ specific hearing requirements. The design should feel intuitive and human-centric. A filter bank is a collection of similar filters that work together sequentially to process audio signals. Audio processing happens in the filter bank, which down-samples incoming audio signals into smaller frequency bands. This process, called decimation, divides sounds into various frequency ranges. Each band receives a certain level of amplification to align with an individual’s hearing profile. Hearing aid filter banks help capture unique sound characteristics that differ from person to person. This ensures that each user receives customized auditory support, enhancing their hearing experience. Frequencies ranging from one hundred twenty-five hertz to eight kilohertz represent average normal hearing, and this entire frequency range is typically segmented into eight kilohertz (Anjali A. Shrivastav, 2020 ). The effectiveness of a hearing aid largely depends on the choice of the filter bank. Different types of filters are described based on their features and assessed for compatibility with hearing aid usage. The goal is to provide hearing capabilities similar to those of a human ear, with the hearing aid adjustable to lower sensitivity frequencies. This is a step toward creating personalized hearing aids that cater to individual needs instead of offering a one-size-fits-all solution. The general diagram of a filter bank is shown in Fig. 4 . 3.2.0 Analogue and Digital filter. Generally, filter banks can be in analogue or digital form. When we compare both characteristics, digital seem to have more advantages over analog. Digital filter bank is versatile, one can write instructions to the filter bank, higher rate of exactness, accurate linear phase, constants changing over time and lastly they occupy a very small silicon area (Anjali A. Shrivastav, 2020 ). 3.2.1 Infinite Impulse Response and Finite Impulse Response Filter. Finite impulse response filter is a type of digital signal processing filter which possess a finite duration, outputs highly depend on current and previous outputs. It is stable because it does not have feedback loops. Finite Impulse Response filters have a phase response which is linear and no phase distortion. That same filter has coefficients which are balanced and even and has low coefficient responsiveness (Anjali A. Shrivastav, 2020 ). Furthermore environmental noise is cancelled and they keep phase triggers in an improved approach for cases where two hearing aids are worn by the user (Huang, Shaoguang, et al, 2015). On the other hand, Infinite Impulse Response filer have feedback loops which lead to unsteadiness if not carefully designed. Infinite Impulse Response has an advantage of having less logic operations hence posing less costs and has a lower stop band attenuation when compared to Finite Impulse Response. With the higher taps of finite impulse response filter bank additional costs also elevate. Considering wattage and hardware resources finite impulse response is costlier. The costly challenge is increased when the width of transition required is small (Zhongxia Shang and Yong Lian, 2018). To guarantee uniform phase and steadiness without being too costly, finite impulse response filters are designed using a direct from transposed (Anjali A. Shrivastav, 2020 ). This method reduces computations but offering high outcomes at the same time. 3.2.2 Uniform and Non Uniform Filter Banks. The titles describe the consistence of subordinate band spacing. Humanized sensation can be illustrated better in an exponential growth. The non-uniform filter imitates human hearing quality better and matches human audiogram (Nisha Haridas ; Elizabeth Elias, 2016). Non uniform filter has a better off subordinate bands allocation giving result to elevated human audiogram matching. The previous point is achieved when there is greater number of subordinate bands. This fact results in complicated components and overhead. Uniform filter bank is not able to detect the specific hearing attributes of various different hearing impaired humans. In terms of auditory matching and fitting uniform filter bank in a no, because it does not give good enough results. After comparing and contrasting these factors non uniform filter is on top notch recognition and highly recommended for use in hearing aid devices. 3.2.3 Non Uniform Fixed Number of Bands Filter Bank and Non-Uniform Variable Bands Filter Bank. When a filter bank has a permanent band number versatility of hearing fitting and matching to human is restricted with very sharp audiograms. This bounds elevation in hearing impairment solution. At the same point, if we increase number of frequency bands it is directly proportional to the increase of wattage consumption and costs. Considering the above factors, a filter bank should have very little number of subordinate bands that can be specifically customized without having massive changing of parameters. In the present day these electronic devices have been produced having a large number of sub ordinate bands of about more than thirty bands in an aim to solve matching error problem. It works but has a large disadvantage of high wattage consumption and very large number of computations. After looking at other works, variable bandwidth filters are better off providing good audiogram matching when compared to non-uniform filter bank. The figure below shows a ranking order of filter banks 3.3 Decimation filter in filter bank Design of filter bank can be made in analog or digital form. In comparison digital filters have more advantages than analog ones. A designer can write instructions to the filter, parallelism can be done, efficiency, achieve small compact size, constant linear phase hence advantageous to analog (Sumedh Dhabu and Achutavarrier Prasad Vinod, 2015 )Finite impulse response filters are mostly used in this section because they are stable, they have few coefficients, linear phase response and less phase distortion when compared to nonlinear phase infinite impulse response IIR filter (Shobhit kumar nema, etal, 2016). This further enhances acoustic noise cancellation and better preserves phase cues for binaural hearing (etal, 2015 ). Decimation refers to the process of reducing a signals sample frequency to a lower sampling frequency. A decimation filter is a one that decreases the sample rate by a factor of m where m is an integer (Niveditha, 2022 ). Decimation filters are widely incorporated in hearing aids because they result in decreased group delay and wattage use. Decimation filters consists of a Comb filter with five stages of integration and differentiation, sixteen as decimation factor and with input of 1.28 megahertz from the sigma to delta convertor (S V V Satyanarayana, 2023 ). The comb filter processes the input signal and outputs eighty kilohertz which is inherited to half band low pass filter as input (S V V Satyanarayana, 2023 ). The half band filter has decimation factor of two hence it outputs forty kilohertz which becomes the sampling rate of corrector filter (S V V Satyanarayana, 2023 ). Corrector FIR filter outputs four kilohertz. 3.4 Performance Parameter for Filter Design and Evaluation Matching error refers to the difference between a person's hearing loss threshold at various frequencies and the optimal threshold in their audiogram. This measurement is vital for assessing the effectiveness of the hearing aid filter design. A smaller matching error indicates better alignment with the user's hearing needs. Achieving this requires tuning the hearing aid to an individual’s specific hearing profile, with a tolerable matching error typically of up to 3dB (A. Amir and Elizabeth Elias, 2019). Higher adaptability is necessary for more precise fittings to improve user comfort. Keeping power consumption low is essential for wearable medical devices. Excessive power use can cause discomfort from overheating and drain the battery faster. Reducing power consumption can extend battery life and enhance user safety. Simplicity is important for minimizing power dissipation. Reducing the number of multipliers helps achieve this simplicity and subsequently lowers power requirements. It's crucial to limit the number of computations to regulate power usage. Some filtering algorithms may be energy-intensive. Since hearing aids are worn, appearance matters. Many users are concerned about the stigma associated with wearing them. Smaller, more compact designs are likely to improve comfort. For closed-fitting hearing aids, group delay should not exceed twenty milliseconds, while for open-fitting aids, it should not go over ten milliseconds. Latency that exceeds these thresholds can affect lip reading. A stop band attenuation greater than sixty dB is required for the filter channels (A. Amir and Elizabeth Elias, 2019). Greater stop band attenuation can help provide more gain before feedback starts and may improve programmability for response magnitude (Anjali A. Shrivastav, 2020 ). 3.4 Cutting Edge Methods for Filter Evaluation and Design In this part of the paper will discuss on methods for filter design including distributed arithmetic design method (Bhagyalakshmi, 2015 ), variable bandwidth filter design technique (Rawandale and Ganorkar, 2023), constrained least square filter design method (V.V Mahesh and T K Shahana, 2018), variable bandwidth filter based on farrow architecture (Nisha Haridas ; Elizabeth Elias, 2016), fractional interpolation design method (Devis, and M. Manuel,, 2021), interpolation, masking and decimation filter design method (Sumedh Dhabu and Achutavarrier Prasad Vinod, 2015 ), frequency response masking based design (Zhongxia Shang and Yong Lian, 2018) and cosine method for filter design (SAJAN P PHILIP, SAMPATH PALANISWAMI, ELANGO SAKER, SHOUKATH ALI K, 2024). 3.4.0 Filter Design Based On Coefficient Decimation. This method involves unchanging filter coefficients and others replaced by zero. This leads to the many frequency bands resulting in less but efficient logical computations and reduced complexity (Anjali Shrivastav, Mahesh kolte etal, 2022). 3.4.1 Design Based on Distributed Arithmetic. Distributed arithmetic based filter design approach less power, smaller size and have a high throughput. The distributed arithmetic method replaces multiplications with a lookup table and a shifter accumulator which results in a solution to these requirements (Sneha Raj, etal, 2016). A design of FIR with low power use and low delay was done by further introducing Offset Binary Code to the distributed arithmetic method (Nagajyothi G & Sridevi S, 2020 ). This method involves replacing multipliers and adders with look up table and shifter accumulator (Sneha Raj and Athira Shaji, 2016 ). Multipliers are logically detailed, they need much area and consume a lot of power in Finite Impulse Response filter. A way to upgrade versatility (Bhagyalakshmi, 2015 ) introduced a finite impulse response filter with lookup table values that change during the program execution time. In (Nagajyothi G & Sridevi S, 2020 ) author introduced a low wattage consuming finite impulse response filter with less area and delay by the use of offset binary code distributed arithmetic method. This method involves the use of signed bits in a shared lookup table. 3.4.2 Design Based on Frequency Response Masking (FRM). Frequency response masking is used when a smaller and less complicated transition band is required for a filter bank. Anjali Shrivastav et al (Anjali Shrivastav, Mahesh kolte etal, 2022) came up with two symmetric half band filters at middle frequency point to avoid larger computations. The structure succeeds in 5dB matching error. Frequency response masking method is used to reduce filter complexity. 3.4.3 Farrow Structure Filter Based Design. This method utilizes implementation of variable fractional delay filters. According to (Nisha Haridas ; Elizabeth Elias, 2016) uses polynomial interpolation method to estimate standard delay result of a filter. This method allows customization of range of frequencies. Because total response is measured in direct collaboration of fixed subordinate filters. In (Nisha Haridas ; Elizabeth Elias, 2016) canonical signed digit was collaborated with farrow structure to remove excess logic operation complexity. 3.4.4 Fractional Interpolation Filter Based Design. This method involves the subordinate division with different band width from one filter. According to (Devis, and M. Manuel,, 2021) fractional interpolation method was applied on a Parks McClellan filter to come up with different sub ordinate bands in the reconfigurable filter bank structure. Tis method results in reduction in number of multipliers and an acceptable group delay. 3.4.5 Filter Based on Cosine Method. This method allows one single filter and shift it using cosine waves to create series of filters each with different frequency range. The authors in (SAJAN P PHILIP, SAMPATH PALANISWAMI, ELANGO SAKER, SHOUKATH ALI K, 2024) made use of a polyphaser structure when designing the filter bank. When combined with general modulation coefficients, reduced logic operations demand and elevated versatility and customization is achieved. The proposed system was designed in a way to tune sound for various types of hearing loss while maintaining group delays at minimum. The method can create more than a thousand different ways to partition sound into frequency bands based on sound signals that match a human hearing audiogram. 3.4.6 Variable Bandwidth Filter Based Design. This method adopts sub bands from a single variable bandwidth filter (Nisha Haridas ; Elizabeth Elias, 2016). Parks Mc Clellan algorithm works when designing an evenly separated filter bandwidth (nisha haridas & elizabeth elias, 2016). Configurability is achieved by adjusting the bandwidth dependent adjustable parameter. Sampling frequency conversion method for a variable filter where ratio of bandwidth can be twisted to change the bandwidth was done (james t george & elizabeth elias, 2014). 3.5 Impact of Digital Filter Design Techniques in Improving Filter Performance This section ventures into several published works in relation to hearing aid decimation filters. In this part illustration of outcomes basing on the key performance variables vi proposed design methods. Not a big number of future work has been pointed out for additional research. These work helps gather insights for designing an acceptable filter in a way to solve problem encountered by hearing aid users. In recent years, considerable work has been undertaken in the domain of hearing aid filter design techniques to improve efficiency. The results are shown by Table 1 below Table 1 Impact of filter design techniques in improving efficiency Filter Bank Design Method or architecture Performance Improvement Reference Gated Diffusion Input Logic Superior area and power efficiency compared to CMOS logic (subbulakshmi n, and R. Manimegalai, 2017 ) Variable bandwidth utilizing farrow structure Low power and low group delay (raghu indrakanti and elizabeth elias, 2018) Signed magnitude architecture 24% area reduction and 33.9% wattage use reduction when compared to signed decimal architecture (Baboji Killard and Sridevi sriadibhata, 2019) Limited least square Finite Impulse Response filter Reduced number of multipliers by sixty four of them when compared to continuous coefficients (V.V Mahesh and T K Shahana, 2018) Limited least square finite impulse response filter Having uncrooked characteristics in bandwidth Reduced delay by 117.9 Pico seconds when compared to the method in (V.V Mahesh and T K Shahana, 2018) (V. V. Mahesh and T. K. Shahana, 2020) Finite response filter focusing on the floating point arithmetic Wattage use reduced by nine percent, delay by twenty one percent, random access memory by forty four percent and adder count by fourteen percent as compared to band pass finite impulse response filter (V.D.M.Jabez Daniel and M.Kanishka, 2019) Smallest phase range filters with reduced number of coefficients Reduced delay by 30.3 picoseconds compared to the limited least square finite impulse response filter in (V. V. Mahesh and T. K. Shahana, 2020) (V. V. Mahesh and T. K. Shahana, 2020) Direct FIR Power use reduced by 2.05% and delay 3.5% lesser compared to IIR (Narem Mohith and Reddy, 2020) IIR using partial fraction technique Reduced filter order hence reduced hardware (K. N. Parvin and M. Z. Hussain) Distributed Arithmetic Lower power and delay compared to direct FIR filter design (Nagajyothi G & Sridevi S, 2020 ) Offset Binary Codie Distributed Arithmetic Lower wattage use and delay compared to ordinary Distributed Arithmetic method (S V V Satyanarayana, 2023 ) Fractional interpolation Low hardware complexity hence low power (A. Amir and Elizabeth Elias, 2019) Hamming window FIR Audiogram matching in most impairments and less matching error (Sushma C and padmaja nimmagadda, 2024) Frequency Response Masking Saved 70% logic operation resources compared to other none Frequency response masking methods (Sajan P Philip and Sampath Palaniswami, 2020) Annealing algorithm in Half band filter Improvement on low latency by 3 to 4% (Zhu, 2021 ) Frequency Response Masking Matching error was improved by 56% throughput improved by 60% (Anjali Shrivastav, Mahesh kolte etal, 2022) Canonic signed digit Hardware architecture reduced by 60% and power reduced by 80% (Niveditha, 2022 ) Intelligent Variable Bandwidth Filter Matching error was reduced by 1%, power was reduced by 2%, delay was reduced by 12% compared to three level filter in (Devis, and M. Manuel,, 2021). (Rawandale and Ganorkar, 2023) Three level filter bank Less power consumption and throughput compared to constrained least square filter (Devis, and M. Manuel,, 2021) Parallel Reconfigurable Filter bank Low processing time achieved (Sushma C & Padmaja N, 2023 ) Interpolated continuously variable fractional delay structure based filter There is a wider cutoff frequency range when compared to fractional delay structure (Sumedh Dhabu and Achutavarrier Prasad Vinod, 2015 ) Neural network based Finite Impulse response filter Improvement in accuracy and delay comparing to present methods (S. Chivapreecha and P. Yospanya, 2024 ) Filter utilizing notch algorithm Filter frequency was compared with convectional filters however logical resources were too much (S. C. Marcrum and E. M. Picou,, 2021) 4 CONCLUSION AND FUTURE PERSPECTIVE Suggestions for further performance improvement in terms of area and power includes replacing multipliers with adders. Using multiplier less filters like Cascaded integrator comb is also a solution. Address can also be replaced with compressors can improve speed and area of FIR filter in hearing aids. Utilizing multirate architecture in filter design also improves efficiency since filters are very sensitive to noise. Variable bandwidth filters utilizing farrow structure, distributed arithmetic combined with offset binary code and use of canonic signed digit can reduce complexity, speed and area of filters. Further studies can be done by elaborating strengths and weaknesses of the techniques above. This approach could lead to innovative solutions and enhance performance and user experience. Declarations Funding N/A Conflict of interest/Competing interests The authors declare that they have no competing interests Ethics approval and consent to participate N/A Consent for publication The authors give consent to publish this work. Data availability N/A Materials availability N/A Code availability N/A Author contribution Rachael Samkange (RS) mainly formulated the research concepts and literature review including formulating the research idea focused on improving hearing aid performance. RC conducted an extensive review of existing digital filter design techniques relevant to hearing aids vs the need for low-delay decimation filters in portable hearing aid devices. Munyaradzi Rushambwa (MR) mainly focused on filter design methods suitable for hearing aids. MR’s work was on hypothesizing on improved decimation filter architectures. Rajkumar Palannipan (RP) focused on the implementability of the decimation filters on hearing aids through performance analysis focusing on delay metrics and energy efficiency by comparing various techniques. Vikneswaran Vijean (VV) focused on the validation of the findings and technical writing suitability. They prepared the findings and illustrations, drafting sections of the paper and technical analysis. Fizza Nabi (FN) focused on the overall coordination and manuscript finalization where they edited the manuscript for clarity, coherence and adherence to this journal. Acknowledgment It is a true honor to express our sincere gratitude to the Harare Institute of Technology library for giving us access to the information we used in this research. Their unwavering support, insightful guidance and inspiring mentorship throughout the work has been invaluable. Without their dedication and encouragement completing this paper within the set time frame would have been incredibly challenging. We deeply appreciative of their contributions and the positive impact they had on this journey. References A. Amir, T. S. Bindiya, and Elizabeth Elias. 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(2016). design of reconfigurable digital filter. international journal of science and research, 5 (7), 40-454. Solomon, Nick, and Pardeep Bhandari. (n.d.). Patent landscape report on assistive devices and technologies for visually and hearing impauired persons. WIPO. subbulakshmi n, and R. Manimegalai. (2017). low power MAC based filter bank using GDI logic for hearing aid. International journal of electronics and electrical engineering, 3 , 235-238. Sumedh Dhabu and Achutavarrier Prasad Vinod. (2015). Design and FPGA implementation of variable cutoff frequency filter based on continuously variable fractionnal delay structure and interpolation technique. international journal of advances in telecommunication, electrotechnics, signals and systems, 4 (3). Sushma C, padmaja nimmagadda. (2024). design of efficient alterable bandwidth FIR filterbank for hearing aid system. advances in electrical engineering, electronis and energy, 7 . Sushma, C., & Padmaja, N. (2023). Design of Low Delay and Low complex Parallel Reconfigurable Filter bank for Hearing aid. IEEEExplore , 1-6. V.D.M.Jabez Daniel and M.Kanishka. (2019). A Computationally Efficient halfband FIR filter bank for hearing aid. international journal for scientific research and development , 1-6. V.V Mahesh, T K Shahana. (2018). Constrained least square nonuniform dynamic filter bank for delay. health and technology , 1-9. V. V. Mahesh · T. K. Shahana. (2020). Design and synthesis of filter bank structures based on low order . health and technology , 1-16. WHO. (2024). deafness and hearing loss. who. WHO. (2025). deafness and hearing loss. WHO. Yong Lian and Ying Wei. (2005). a computationally efficient nonuniform FIR digital filter bank for hearing aids. IEEE transactions on circuits and systems, 52 (12), 2754-2762. Z. Shang, Y.Lian and Y. Zhao. (2017). low power FIR filter design for wearable devices using frequency response masking technique. 12th international conference on ASIC (ASICON). GUIYANG. Zhongxia Shang, Yang Zhao, and Yong Lian. (2018). Low power FIR filter design for wearable devices using frequency response masking technique. international conference on ASIC. Zhu, X. (2021). cost effective filter design for low latency audio analogue to digital convertor. birmingham. Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8200058","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Systematic Review","associatedPublications":[],"authors":[{"id":550381715,"identity":"1e61d9ce-989e-4f00-9194-86ab4fb4835d","order_by":0,"name":"MUNYARADZI CHARLES 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2","display":"","copyAsset":false,"role":"figure","size":35739,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIllustration of hearing aid development for the hearing impaired\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8200058/v1/6c8648779a094ec48506576c.png"},{"id":96784309,"identity":"2762d24e-de31-4a6a-88d5-81c4dd1d7e41","added_by":"auto","created_at":"2025-11-26 05:31:46","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":20914,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHearing aid system\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8200058/v1/2458ba6702eb73b4ee5b2ad8.png"},{"id":96784310,"identity":"74ac12d0-f4e7-41f8-9256-c17db1943dec","added_by":"auto","created_at":"2025-11-26 05:31:46","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":36170,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGeneral diagram for filter bank\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-8200058/v1/103ee1ca5f02eb7bf3718406.png"},{"id":96915342,"identity":"5d274d8f-90f2-4ba9-b4ac-472a1edab07f","added_by":"auto","created_at":"2025-11-27 14:07:08","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":16976,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHierarchy showing types of filter banks for hearing aids\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-8200058/v1/edcd009b4bacf05b29a5791a.png"},{"id":96784314,"identity":"450409fd-9262-47b3-9a1f-73b79eddfac0","added_by":"auto","created_at":"2025-11-26 05:31:46","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":63942,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFilter bank illustration\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-8200058/v1/63dc79c0014ef103c10258f4.png"},{"id":96923025,"identity":"4c183f07-1086-42be-97b3-4aa34005cf64","added_by":"auto","created_at":"2025-11-27 14:20:36","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1198653,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8200058/v1/bc107abb-70cc-4fda-864a-727a729db2d1.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eDigital Filter Design Methods for Hearing Aid Performance Improvement\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"1. INTRODUCTION","content":"\u003cp\u003eWorld Health Organization gave a report outlining that (WHO, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), In the whole world, around 5% of the world\u0026rsquo;s population is affected by hearing impairments. According to (Yong Lian and Ying Wei, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2005\u003c/span\u003e), a person will not pick sound signals that fall below a minimum level also known as hearing threshold. A hearing impaired person cannot hear well as others with hearing threshold of twenty decibels or better in both ears (WHO, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Hearing impairment is not a one size fits all type of situation, it is in different forms (Pandey sidheshwar, Fayez Bahmad Jr, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Three main types are sensoneurial, conductive and hybrid hearing loss (Pandey sidheshwar, Fayez Bahmad Jr, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e gives an illustration of the sages of hearing loss and the hearing threshold in decibels. For mild hearing loss one will be facing difficulty in understanding normal speech. For hearing threshold of forty to seventy decibels there will be difficulty in understanding loud speech\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eSevere hearing loss occurs when a person can only understand amplified speech. Hearing thresholds above ninety decibels present challenges in comprehending amplified sounds, which indicates profound hearing loss. Hearing impairment can affect one or both ears. Patients face increasing hearing thresholds, limited frequency range, reduced sound quality, and restricted sensitivity. Hearing aid devices can greatly help patients with mild to profound hearing loss communicate better. These electronic devices compensate for the weakening ability of the human ear, improving the quality of sound signals for patients. Clear speech becomes much easier to hear when sound signals are transmitted at suitable volumes. Hearing aids are designed to amplify and process audio signals to match a person\u0026rsquo;s hearing profile, helping to address the difficulties caused by hearing loss. Research by the World Intellectual Property Organization (WIPO) highlights efforts to develop innovative solutions for individuals with hearing loss, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e (Solomon \u0026amp; Pardeep Bhandari).\u003c/p\u003e\u003cp\u003eThe major causes of hearing loss include genetics, exposure to toxic substances, excessive noise, and aging. The World Health Organization predicts that by 2050, around seven hundred million people may experience hearing loss. Many children with severe hearing impairment struggle to access education in developing countries. Hearing aids can provide significant assistance to those who are deaf, but they are often only available to individuals in industrialized nations. If untreated, hearing loss may lead to Alzheimer\u0026rsquo;s disease or dementia, as reported by Johns Hopkins, indicating a connection between these conditions (Hopkins, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eAdvancements in digital signal processing are making modern hearing aids more versatile, smaller, and more widely used. These devices can amplify audio signals based on the user's specific hearing needs, acting as a delicate aid for those with hearing impairment. The core of this development lies in the design, which influences how sound is processed and delivered. Filters in hearing aids perform various functions, such as enhancing speech clarity, reducing background noise, and fine-tuning frequency responses to meet the user\u0026rsquo;s hearing profile. The design of these filters must consider power efficiency, latency, performance, and size. If the delay exceeds twenty milliseconds, it can hinder lip reading and diminish user satisfaction with the hearing aid (Justin R. Burwinkel, Au.D.). Analogue and digital hearing aids are two types available. Analogue hearing aids change the input signal into electrical signals whereas digital hearing aids change the input audio signal into binary numerical information (S V V Satyanarayana, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Digital hearing aids are more preferred over analogue because they are adaptable and self-adjusting. There are two basic types of digital filters which are used in hearing aid design. Infinite impulse response (IIR) are more reliable in terms of memory an computational complexity however can bring about distortion problem if not designed with care (risla fathima p, pranav kumar m p, 2019). Finite Impulse Response (FIR) filter is mostly chosen because of it being stable, sensible to coefficients and linear phase property (Z. Shang, Y.Lian and Y. Zhao, 2017). The massive increase in the number of individuals with hearing loss and difficult situations they meet pin points a case of emergency to advance and improve hearing aid technology. Out there those who should be having hearing aids are moving without them because the existing hearing devices possess some characteristics and limitations. n of digital filter design a very important the Engineers and researchers have taken studies on filter design methods each making a significant input in improving performance of the hearing aid.\u003c/p\u003e\u003cp\u003eThis paper gives an overview of some challenges faced by hearing aid users, such as battery consumption and throughput. The filter bank often being essential for enhancing digital hearing aids, requires careful attention to improve performance. This study is concentrated on analyzing hearing aid filter designing methods. Human natural audiogram have a hearing range of twenty hertz to twenty kilohertz, however the ear is more responsive in the four kilohertz band.\u003c/p\u003e"},{"header":"2. REVIEW METHODOLOGY","content":"\u003cp\u003eThe significant rise in the number of individuals with hearing loss, along with the challenges they face, underscores the urgent need to develop and improve hearing aid technology. Many people who could benefit from hearing aids do not have access to them because current devices still have limitations. Researchers and engineers have studied filter design methods to enhance hearing aid performance. Resources such as Research4life, Google Scholar, Science Direct, and other online databases were used to find articles and materials for analysis. The key search terms focused on digital filter types and their design techniques to ensure the hearing aid filter met necessary characteristics. All design methods were validated and compared. The study excluded research published before 2010 and any articles that hadn't undergone peer review. From the search, two hundred papers were identified. Fifty duplicates were removed during the initial screening. After evaluating the relevance of abstracts, ninety-five of the remaining one hundred and fifty articles were eliminated. Consequently, this paper is based on thirty journal papers. The evaluation of design techniques focused on characteristics offered by digital filters when processing audio signals.\u003c/p\u003e"},{"header":"3. METHODS OF DESIGNING THE FILTER BANK AND THE HEARING AID SYSTEM","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Part Played by Hearing Aid in Reducing Hearing Impairment\u003c/h2\u003e\u003cp\u003eSensorineural Hearing Loss (SNHL) is the most common type of hearing loss. In this condition, tiny hair cells in the cochlea of the inner ear can no longer effectively detect certain sound frequencies. Typically, individuals with sensorineural hearing loss find it difficult to hear clearly in noisy surroundings. Hearing aids help by amplifying specific sound signals that correspond to human hearing abilities. The below figure illustrates the hearing aid system, including amplification, conversion, filtering, and output of audio signals.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eA hearing aid is designed to capture audible signals using a microphone. The microphone converts weak audio signals into stronger ones. These amplified signals are then sent to the ear through a speaker. Digital hearing aids enhance interaction, making communication clearer and more comfortable. Signal processing algorithms serve to improve speech sounds, which are essential in daily life. A hearing aid consists of an amplifier that strengthens faint sounds so they become loud and clear. The filter bank is the heart of the hearing aid, filtering out background noise to emphasize speech. The hearing aid processes audio signals by adjusting amplitude, shifting frequencies, and applying compression for a smoother and more natural output (Prerna Kumari, Deepak Singh, 2021). A crucial feature of hearing aids is noise cancellation, which enables users to focus on important sounds. Engineers face several challenges in designing hearing aids, including the need for small integrated circuits, low power consumption, improvements in sound balance, and managing high peak power demands as wireless features become more common. They must also prevent frequency overlap, minimize background noise in sensitive components like filters, maintain a narrow transition width, and reduce the number of logical operations.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Hearing Aid Filter Bank\u003c/h2\u003e\u003cp\u003eThe challenges researchers face highlights the need for hearing devices to be tailored to meet individual users\u0026rsquo; specific hearing requirements. The design should feel intuitive and human-centric. A filter bank is a collection of similar filters that work together sequentially to process audio signals. Audio processing happens in the filter bank, which down-samples incoming audio signals into smaller frequency bands. This process, called decimation, divides sounds into various frequency ranges. Each band receives a certain level of amplification to align with an individual\u0026rsquo;s hearing profile. Hearing aid filter banks help capture unique sound characteristics that differ from person to person. This ensures that each user receives customized auditory support, enhancing their hearing experience. Frequencies ranging from one hundred twenty-five hertz to eight kilohertz represent average normal hearing, and this entire frequency range is typically segmented into eight kilohertz (Anjali A. Shrivastav, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The effectiveness of a hearing aid largely depends on the choice of the filter bank. Different types of filters are described based on their features and assessed for compatibility with hearing aid usage. The goal is to provide hearing capabilities similar to those of a human ear, with the hearing aid adjustable to lower sensitivity frequencies. This is a step toward creating personalized hearing aids that cater to individual needs instead of offering a one-size-fits-all solution. The general diagram of a filter bank is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e3.2.0 Analogue and Digital filter.\u003c/h2\u003e\u003cp\u003eGenerally, filter banks can be in analogue or digital form. When we compare both characteristics, digital seem to have more advantages over analog. Digital filter bank is versatile, one can write instructions to the filter bank, higher rate of exactness, accurate linear phase, constants changing over time and lastly they occupy a very small silicon area (Anjali A. Shrivastav, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\u003cdiv id=\"Sec7\" class=\"Section3\"\u003e\u003ch2\u003e3.2.1 Infinite Impulse Response and Finite Impulse Response Filter.\u003c/h2\u003e\u003cp\u003eFinite impulse response filter is a type of digital signal processing filter which possess a finite duration, outputs highly depend on current and previous outputs. It is stable because it does not have feedback loops. Finite Impulse Response filters have a phase response which is linear and no phase distortion. That same filter has coefficients which are balanced and even and has low coefficient responsiveness (Anjali A. Shrivastav, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Furthermore environmental noise is cancelled and they keep phase triggers in an improved approach for cases where two hearing aids are worn by the user (Huang, Shaoguang, et al, 2015). On the other hand, Infinite Impulse Response filer have feedback loops which lead to unsteadiness if not carefully designed. Infinite Impulse Response has an advantage of having less logic operations hence posing less costs and has a lower stop band attenuation when compared to Finite Impulse Response. With the higher taps of finite impulse response filter bank additional costs also elevate. Considering wattage and hardware resources finite impulse response is costlier. The costly challenge is increased when the width of transition required is small (Zhongxia Shang and Yong Lian, 2018). To guarantee uniform phase and steadiness without being too costly, finite impulse response filters are designed using a direct from transposed (Anjali A. Shrivastav, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). This method reduces computations but offering high outcomes at the same time.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section3\"\u003e\u003ch2\u003e3.2.2 Uniform and Non Uniform Filter Banks.\u003c/h2\u003e\u003cp\u003eThe titles describe the consistence of subordinate band spacing. Humanized sensation can be illustrated better in an exponential growth. The non-uniform filter imitates human hearing quality better and matches human audiogram (Nisha Haridas ; Elizabeth Elias, 2016). Non uniform filter has a better off subordinate bands allocation giving result to elevated human audiogram matching. The previous point is achieved when there is greater number of subordinate bands. This fact results in complicated components and overhead. Uniform filter bank is not able to detect the specific hearing attributes of various different hearing impaired humans. In terms of auditory matching and fitting uniform filter bank in a no, because it does not give good enough results. After comparing and contrasting these factors non uniform filter is on top notch recognition and highly recommended for use in hearing aid devices.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section3\"\u003e\u003ch2\u003e3.2.3 Non Uniform Fixed Number of Bands Filter Bank and Non-Uniform Variable Bands Filter Bank.\u003c/h2\u003e\u003cp\u003eWhen a filter bank has a permanent band number versatility of hearing fitting and matching to human is restricted with very sharp audiograms. This bounds elevation in hearing impairment solution. At the same point, if we increase number of frequency bands it is directly proportional to the increase of wattage consumption and costs. Considering the above factors, a filter bank should have very little number of subordinate bands that can be specifically customized without having massive changing of parameters. In the present day these electronic devices have been produced having a large number of sub ordinate bands of about more than thirty bands in an aim to solve matching error problem. It works but has a large disadvantage of high wattage consumption and very large number of computations. After looking at other works, variable bandwidth filters are better off providing good audiogram matching when compared to non-uniform filter bank. The figure below shows a ranking order of filter banks\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Decimation filter in filter bank\u003c/h2\u003e\u003cp\u003eDesign of filter bank can be made in analog or digital form. In comparison digital filters have more advantages than analog ones. A designer can write instructions to the filter, parallelism can be done, efficiency, achieve small compact size, constant linear phase hence advantageous to analog (Sumedh Dhabu and Achutavarrier Prasad Vinod, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2015\u003c/span\u003e)Finite impulse response filters are mostly used in this section because they are stable, they have few coefficients, linear phase response and less phase distortion when compared to nonlinear phase infinite impulse response IIR filter (Shobhit kumar nema, etal, 2016). This further enhances acoustic noise cancellation and better preserves phase cues for binaural hearing (etal, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Decimation refers to the process of reducing a signals sample frequency to a lower sampling frequency. A decimation filter is a one that decreases the sample rate by a factor of m where m is an integer (Niveditha, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Decimation filters are widely incorporated in hearing aids because they result in decreased group delay and wattage use. Decimation filters consists of a Comb filter with five stages of integration and differentiation, sixteen as decimation factor and with input of 1.28 megahertz from the sigma to delta convertor (S V V Satyanarayana, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The comb filter processes the input signal and outputs eighty kilohertz which is inherited to half band low pass filter as input (S V V Satyanarayana, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The half band filter has decimation factor of two hence it outputs forty kilohertz which becomes the sampling rate of corrector filter (S V V Satyanarayana, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Corrector FIR filter outputs four kilohertz.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e3.4 Performance Parameter for Filter Design and Evaluation\u003c/h2\u003e\u003cp\u003eMatching error refers to the difference between a person's hearing loss threshold at various frequencies and the optimal threshold in their audiogram. This measurement is vital for assessing the effectiveness of the hearing aid filter design. A smaller matching error indicates better alignment with the user's hearing needs. Achieving this requires tuning the hearing aid to an individual\u0026rsquo;s specific hearing profile, with a tolerable matching error typically of up to 3dB (A. Amir and Elizabeth Elias, 2019). Higher adaptability is necessary for more precise fittings to improve user comfort. Keeping power consumption low is essential for wearable medical devices. Excessive power use can cause discomfort from overheating and drain the battery faster. Reducing power consumption can extend battery life and enhance user safety. Simplicity is important for minimizing power dissipation. Reducing the number of multipliers helps achieve this simplicity and subsequently lowers power requirements. It's crucial to limit the number of computations to regulate power usage. Some filtering algorithms may be energy-intensive. Since hearing aids are worn, appearance matters. Many users are concerned about the stigma associated with wearing them. Smaller, more compact designs are likely to improve comfort. For closed-fitting hearing aids, group delay should not exceed twenty milliseconds, while for open-fitting aids, it should not go over ten milliseconds. Latency that exceeds these thresholds can affect lip reading. A stop band attenuation greater than sixty dB is required for the filter channels (A. Amir and Elizabeth Elias, 2019). Greater stop band attenuation can help provide more gain before feedback starts and may improve programmability for response magnitude (Anjali A. Shrivastav, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e3.4 Cutting Edge Methods for Filter Evaluation and Design\u003c/h2\u003e\u003cp\u003eIn this part of the paper will discuss on methods for filter design including distributed arithmetic design method (Bhagyalakshmi, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), variable bandwidth filter design technique (Rawandale and Ganorkar, 2023), constrained least square filter design method (V.V Mahesh and T K Shahana, 2018), variable bandwidth filter based on farrow architecture (Nisha Haridas ; Elizabeth Elias, 2016), fractional interpolation design method (Devis, and M. Manuel,, 2021), interpolation, masking and decimation filter design method (Sumedh Dhabu and Achutavarrier Prasad Vinod, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), frequency response masking based design (Zhongxia Shang and Yong Lian, 2018) and cosine method for filter design (SAJAN P PHILIP, SAMPATH PALANISWAMI, ELANGO SAKER, SHOUKATH ALI K, 2024).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e3.4.0 Filter Design Based On Coefficient Decimation.\u003c/h2\u003e\u003cp\u003eThis method involves unchanging filter coefficients and others replaced by zero. This leads to the many frequency bands resulting in less but efficient logical computations and reduced complexity (Anjali Shrivastav, Mahesh kolte etal, 2022).\u003c/p\u003e\u003cdiv id=\"Sec14\" class=\"Section3\"\u003e\u003ch2\u003e3.4.1 Design Based on Distributed Arithmetic.\u003c/h2\u003e\u003cp\u003eDistributed arithmetic based filter design approach less power, smaller size and have a high throughput. The distributed arithmetic method replaces multiplications with a lookup table and a shifter accumulator which results in a solution to these requirements (Sneha Raj, etal, 2016). A design of FIR with low power use and low delay was done by further introducing Offset Binary Code to the distributed arithmetic method (Nagajyothi G \u0026amp; Sridevi S, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). This method involves replacing multipliers and adders with look up table and shifter accumulator (Sneha Raj and Athira Shaji, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Multipliers are logically detailed, they need much area and consume a lot of power in Finite Impulse Response filter. A way to upgrade versatility (Bhagyalakshmi, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) introduced a finite impulse response filter with lookup table values that change during the program execution time. In (Nagajyothi G \u0026amp; Sridevi S, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) author introduced a low wattage consuming finite impulse response filter with less area and delay by the use of offset binary code distributed arithmetic method. This method involves the use of signed bits in a shared lookup table.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section3\"\u003e\u003ch2\u003e3.4.2 Design Based on Frequency Response Masking (FRM).\u003c/h2\u003e\u003cp\u003eFrequency response masking is used when a smaller and less complicated transition band is required for a filter bank. Anjali Shrivastav et al (Anjali Shrivastav, Mahesh kolte etal, 2022) came up with two symmetric half band filters at middle frequency point to avoid larger computations. The structure succeeds in 5dB matching error. Frequency response masking method is used to reduce filter complexity.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section3\"\u003e\u003ch2\u003e3.4.3 Farrow Structure Filter Based Design.\u003c/h2\u003e\u003cp\u003eThis method utilizes implementation of variable fractional delay filters. According to (Nisha Haridas ; Elizabeth Elias, 2016) uses polynomial interpolation method to estimate standard delay result of a filter. This method allows customization of range of frequencies. Because total response is measured in direct collaboration of fixed subordinate filters. In (Nisha Haridas ; Elizabeth Elias, 2016) canonical signed digit was collaborated with farrow structure to remove excess logic operation complexity.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section3\"\u003e\u003ch2\u003e3.4.4 Fractional Interpolation Filter Based Design.\u003c/h2\u003e\u003cp\u003eThis method involves the subordinate division with different band width from one filter. According to (Devis, and M. Manuel,, 2021) fractional interpolation method was applied on a Parks McClellan filter to come up with different sub ordinate bands in the reconfigurable filter bank structure. Tis method results in reduction in number of multipliers and an acceptable group delay.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section3\"\u003e\u003ch2\u003e3.4.5 Filter Based on Cosine Method.\u003c/h2\u003e\u003cp\u003eThis method allows one single filter and shift it using cosine waves to create series of filters each with different frequency range. The authors in (SAJAN P PHILIP, SAMPATH PALANISWAMI, ELANGO SAKER, SHOUKATH ALI K, 2024) made use of a polyphaser structure when designing the filter bank. When combined with general modulation coefficients, reduced logic operations demand and elevated versatility and customization is achieved. The proposed system was designed in a way to tune sound for various types of hearing loss while maintaining group delays at minimum. The method can create more than a thousand different ways to partition sound into frequency bands based on sound signals that match a human hearing audiogram.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section3\"\u003e\u003ch2\u003e3.4.6 Variable Bandwidth Filter Based Design.\u003c/h2\u003e\u003cp\u003eThis method adopts sub bands from a single variable bandwidth filter (Nisha Haridas ; Elizabeth Elias, 2016). Parks Mc Clellan algorithm works when designing an evenly separated filter bandwidth (nisha haridas \u0026amp; elizabeth elias, 2016). Configurability is achieved by adjusting the bandwidth dependent adjustable parameter. Sampling frequency conversion method for a variable filter where ratio of bandwidth can be twisted to change the bandwidth was done (james t george \u0026amp; elizabeth elias, 2014).\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003e3.5 Impact of Digital Filter Design Techniques in Improving Filter Performance\u003c/h2\u003e\u003cp\u003eThis section ventures into several published works in relation to hearing aid decimation filters. In this part illustration of outcomes basing on the key performance variables vi proposed design methods. Not a big number of future work has been pointed out for additional research. These work helps gather insights for designing an acceptable filter in a way to solve problem encountered by hearing aid users. In recent years, considerable work has been undertaken in the domain of hearing aid filter design techniques to improve efficiency. The results are shown by Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e below\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eImpact of filter design techniques in improving efficiency\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFilter Bank Design Method or architecture\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePerformance Improvement\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGated Diffusion Input Logic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSuperior area and power efficiency compared to CMOS logic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(subbulakshmi n, and R. Manimegalai, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2017\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable bandwidth utilizing farrow structure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLow power and low group delay\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(raghu indrakanti and elizabeth elias, 2018)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSigned magnitude architecture\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e24% area reduction and 33.9% wattage use reduction when compared to signed decimal architecture\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(Baboji Killard and Sridevi sriadibhata, 2019)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLimited least square Finite Impulse Response filter\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eReduced number of multipliers by sixty four of them when compared to continuous coefficients\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(V.V Mahesh and T K Shahana, 2018)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLimited least square finite impulse response filter Having uncrooked characteristics in bandwidth\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eReduced delay by 117.9 Pico seconds when compared to the method in (V.V Mahesh and T K Shahana, 2018)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(V. V. Mahesh and T. K. Shahana, 2020)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFinite response filter focusing on the floating point arithmetic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWattage use reduced by nine percent, delay by twenty one percent, random access memory by forty four percent and adder count by fourteen percent as compared to band pass finite impulse response filter\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(V.D.M.Jabez Daniel and M.Kanishka, 2019)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSmallest phase range filters with reduced number of coefficients\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eReduced delay by 30.3 picoseconds compared to the limited least square finite impulse response filter in (V. V. Mahesh and T. K. Shahana, 2020)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(V. V. Mahesh and T. K. Shahana, 2020)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDirect FIR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePower use reduced by 2.05% and delay 3.5% lesser compared to IIR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(Narem Mohith and Reddy, 2020)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIIR using partial fraction technique\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eReduced filter order hence reduced hardware\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(K. N. Parvin and M. Z. Hussain)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDistributed Arithmetic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLower power and delay compared to direct FIR filter design\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(Nagajyothi G \u0026amp; Sridevi S, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2020\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOffset Binary Codie Distributed Arithmetic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLower wattage use and delay compared to ordinary Distributed Arithmetic method\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(S V V Satyanarayana, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2023\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFractional interpolation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLow hardware complexity hence low power\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(A. Amir and Elizabeth Elias, 2019)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHamming window FIR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAudiogram matching in most impairments and less matching error\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(Sushma C and padmaja nimmagadda, 2024)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFrequency Response Masking\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSaved 70% logic operation resources compared to other none Frequency response masking methods\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(Sajan P Philip and Sampath Palaniswami, 2020)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAnnealing algorithm in Half band filter\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eImprovement on low latency by 3 to 4%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(Zhu, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2021\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFrequency Response Masking\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMatching error was improved by 56% throughput improved by 60%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(Anjali Shrivastav, Mahesh kolte etal, 2022)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCanonic signed digit\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHardware architecture reduced by 60% and power reduced by 80%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(Niveditha, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2022\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIntelligent Variable Bandwidth Filter\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMatching error was reduced by 1%, power was reduced by 2%, delay was reduced by 12% compared to three level filter in (Devis, and M. Manuel,, 2021).\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(Rawandale and Ganorkar, 2023)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eThree level filter bank\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLess power consumption and throughput compared to constrained least square filter\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(Devis, and M. Manuel,, 2021)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eParallel Reconfigurable Filter bank\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLow processing time achieved\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(Sushma C \u0026amp; Padmaja N, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2023\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInterpolated continuously variable fractional delay structure based filter\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eThere is a wider cutoff frequency range when compared to fractional delay structure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(Sumedh Dhabu and Achutavarrier Prasad Vinod, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2015\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNeural network based Finite Impulse response filter\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eImprovement in accuracy and delay comparing to present methods\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(S. Chivapreecha and P. Yospanya, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2024\u003c/span\u003e )\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFilter utilizing notch algorithm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFilter frequency was compared with convectional filters however logical resources were too much\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(S. C. Marcrum and E. M. Picou,, 2021)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"4 CONCLUSION AND FUTURE PERSPECTIVE","content":"\u003cp\u003eSuggestions for further performance improvement in terms of area and power includes replacing multipliers with adders. Using multiplier less filters like Cascaded integrator comb is also a solution. Address can also be replaced with compressors can improve speed and area of FIR filter in hearing aids. Utilizing multirate architecture in filter design also improves efficiency since filters are very sensitive to noise. Variable bandwidth filters utilizing farrow structure, distributed arithmetic combined with offset binary code and use of canonic signed digit can reduce complexity, speed and area of filters. Further studies can be done by elaborating strengths and weaknesses of the techniques above. This approach could lead to innovative solutions and enhance performance and user experience.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eN/A\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest/Competing interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eN/A\u003c/p\u003e\n\u003cp\u003eConsent for publication\u003c/p\u003e\n\u003cp\u003eThe authors give consent to publish this work.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eN/A\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMaterials availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eN/A\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCode availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eN/A\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRachael Samkange (RS) mainly formulated the research concepts and literature review including formulating the research idea focused on improving hearing aid performance. RC conducted an extensive review of existing digital filter design techniques relevant to hearing aids vs the need for low-delay decimation filters in portable hearing aid devices. Munyaradzi Rushambwa (MR) mainly focused on filter design methods suitable for hearing aids. MR\u0026rsquo;s work was on hypothesizing on improved decimation filter architectures. Rajkumar Palannipan (RP) focused on the implementability of the decimation filters on hearing aids through performance analysis focusing on delay metrics and energy efficiency by comparing various techniques. Vikneswaran Vijean (VV) focused on the validation of the findings and technical writing suitability. They prepared the findings and illustrations, drafting sections of the paper and technical analysis. \u0026nbsp;Fizza Nabi (FN) focused on the overall coordination and manuscript finalization where they edited the manuscript for clarity, coherence and adherence to this journal.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgment\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIt is a true honor to express our sincere gratitude to the Harare Institute of Technology library for giving us access to the information we used in this research. Their unwavering support, insightful guidance and inspiring mentorship throughout the work has been invaluable. Without their dedication and encouragement completing this paper within the set time frame would have been incredibly challenging. We deeply appreciative of their contributions and the positive impact they had on this journey.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eA. Amir, T. S. 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Low power FIR filter design for wearable devices using frequency response masking technique. \u003cem\u003einternational conference on ASIC.\u003c/em\u003e \u003c/li\u003e\n\u003cli\u003eZhu, X. (2021). \u003cem\u003ecost effective filter design for low latency audio analogue to digital convertor.\u003c/em\u003e birmingham.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"audio, digital signal processing, filter bank, hearing aid, speech signal","lastPublishedDoi":"10.21203/rs.3.rs-8200058/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8200058/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn recent years, many people have been affected with hearing problems, yet the effect is not significant on human lives. This is where a hearing aid comes into rescue where signals are processed such that the human audiogram is able to pick them. These electronic devices should have a specific design characteristic since they are wearable. Design of these portable devices requires less hardware, low delay and low power use. These characteristics highly depend on the makeup of the hearing aid heart which is the filter bank. This paper reviews the different design techniques for a low delay and energy efficient decimation filter for se in hearing aids for performance analysis.\u003c/p\u003e","manuscriptTitle":"Digital Filter Design Methods for Hearing Aid Performance Improvement","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-26 05:31:42","doi":"10.21203/rs.3.rs-8200058/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"22eb6d6d-8270-4f33-bf18-4c2927131974","owner":[],"postedDate":"November 26th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":58621516,"name":"Biomedical Engineering"}],"tags":[],"updatedAt":"2025-11-26T05:31:42+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-26 05:31:42","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8200058","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8200058","identity":"rs-8200058","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}