Abstract

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Title:: Speech detection method based on a multi-window analysis

Author(s):: Luo Xinwei¹; Liu Ting¹; Huang Ming¹; Xu Xiaogang¹Cao Hongli¹; Bai Xianghua²; Xu Dayong²; ¹Key Laboratory of Underwater Acoustic Signal Processing of Ministry of Education, Southeast University, Nanjing 210096, China
²The 1st Military Representative Office in Nanjing, Shanghai Bureau, PLA Navy MilitaryEquipment Department, Nanjing 210000, China

Keywords:: voice activity detection; multi-window spectral analysis; K-means clustering; threshold adjustment; sequential decision

PACS:: TN911.72

DOI:: 10.3969/j.issn.1003-7985.2021.04.001

Abstract:: Aiming at the poor performance of speech signal detection at low signal-to-noise ratio(SNR), a method is proposed to detect active speech frames based on multi-window time-frequency(T-F)diagrams. First, the T-F diagram of the signal is calculated based on a multi-window T-F analysis, and a speech test statistic is constructed based on the characteristic difference between the signal and background noise. Second, the dynamic double-threshold processing is used for preliminary detection, and then the global double-threshold value is obtained using K-means clustering. Finally, the detection results are obtained by sequential decision. The experimental results show that the overall performance of the method is better than that of traditional methods under various SNR conditions and background noises. This method also has the advantages of low complexity, strong robustness, and adaptability to multi-national languages.

References:

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