An audio endpoint detection method in a low shipborne SNR environment

DOI:	10.11809/bqzbgcxb2023.03.028
Keywords:	audio endpoint detection; multi window spectral subtraction; Mel frequency cepstral coefficients (MFCC); Gammatone frequency cepstral coefficients (GFCC); Naive Bayes
Abstract:	Aiming at a low accuracy and robustness of audio endpoint detection in a shipborne environment, this paper proposes an audio endpoint detection method combining spectral subtraction and naive Bayes classifier. Firstly, the pure audio signals MFCC0 and GFCC0 are extracted to construct fusion features, and, together with the energy entropy ratio feature, they are used as the input of the naive Bayes classifier for training and modeling.Then, the multi window spectral subtraction is used to improve the SNR of the signals with noise to be measured. The signal related features are extracted, and the naive Bayes classifier determines the type of the signal according to the characteristics of the signals to be tested. The simulation results show that, compared with the traditional method, the algorithm effectively reduces false detection and missed detection for low SNR frequency signals with noise on shipboard, which has better accuracy and robustness.
Published:	2023-03-28
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