Acoustic features extraction of abnormal sounds in public places with low signal-to-noise ratio

In order to obtain the acoustic features of abnormal sounds in public places with low signal-to-noise ratio,we propose an empirical wavelet filter bank for extracting acoustic features of abnormal sounds.First,the center frequencies of the filters of the filter bank are obtained based on the human ear hearing characteristics of the equivalent rectangular bandwidth.The boundaries of the empirical wavelet filter bank are calculated.Then,the boundaries are introduced into the empirical wavelet scale function and scaling function to form the empirical wavelet filter bank.At last,an empirical wavelet filter bank is used to decompose the abnormal noise in public places under low signal-to-noise ratio.The normalized logarithmic energy of each decomposed modal is used as the abnormal acoustic acoustics for classification and recognition.The experiments show that compared with the typical speech signal processing and time-frequency signal processing methods,the empirical wavelet filter proposed in this study improves the average recognition rate by 4.75%~37.92%in 0 dB SNR shops,banks,offices and ATMs the validity of this method is confirmed.