Coal–gangue recognition via Multi–branch convolutional neural network based on MFCC in noisy environment

This paper mainly studies the more accurate recognition of coal–gangue in the noise site environment in the process of top coal caving. Mel Frequency Cepstrum Coefficients (MFCC) smoothing method was introduced in the coal–gangue recognition site. Then, a convolution neural network model with three branches was developed. Experiments show that the proposed coal–gangue recognition method based on multi branch convolution neural network and MFCC smoothing can not only recognize the state of falling coal or gangue, but also recognize the operational state of site device.