Hybrid feature extraction method of MFCC+GFCC helicopter noise based on wavelet decomposition

Abstract Aiming at the issue that the recognition accuracy of traditional acoustic signal features is low for helicopter acoustic signals with wind noise in the near field, a method of extracting mixed noise features of MFCC+GFCC based on wavelet decomposition is proposed. Firstly, the three-layer wavelet decomposition and reconstruction are applied to the helicopter acoustic signals; then, the Mel-Frequency Cepstral Coefficients (MFCC) and Gammatone-Frequency Cepstrum Coefficient (GFCC) are respectively extracted for the approximation and detail components; next, the coefficients of detail components which are averaged are combined with those of approximation components to form the hybrid feature parameters; finally, the convolutional neural network is used to classify the signal, to realize the correct recognition of helicopter acoustic signals. Experimental results show that the recognition accuracy is improved by almost 40% in contrast with other traditional methods, such as MFCC and GFCC, when the SNR is equal to -5dB. Further, When the SNR is -10dB, the recognition accuracy is more than 49%, while the traditional methods cannot effectively recognize the helicopter acoustic targets. The proposed feature extraction method can significantly improve the recognition accuracy in the low SNR environment, and provide a reference for near-field detection and recognition of helicopter acoustic targets.