Coal-rock Interface Recognition Based on MFCC and Neural Network

To solve the difficulty in recognition coal-rock interface for the top coal caving process, we proposed a new method based on Mel-frequency cepstrum coefficient (MFCC) and neural network. In this paper, we conducted the noise separation by Independent Component Analysis (ICA) for acoustic signal. Then we extracted MFCC as the feature and recognized the coal-rock interface via BP neural network. The result shows that MFCC reflect the voice features of coal-rock more effectively, comparing to other features (frame energy and kurtosis), it provides average relative reductions of 12% and 19% in error rate, which recognition rate is 83%. We conclude that the method based on MFCC and neural network is an effectively and automatically detection for the coal-rock interface recognitions.

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