Multiple sound event detection based on auditory fusion features
In order to improve the performance of multi-sound event detection task,this paper conducts an in-depth study of the Cascade of Asymmetric Resonators with Fast-Acting Compression(CARFAC)digital co-chlear model,and proposes a multi-sound event detection method based on auditory fusion features.Initially,the CARFAC is employed to extract the Neural Activity Pattern(NAP)of mixed sound.Subsequently,the NAP is concatenated with Gammatone Frequency Cepstral Coefficients(GFCC)to generate fused auditory features,which are then fed into a Convolutional Recurrent Neural Network(CRNN)for fully supervised learning to detect urban sound events.Experimental results demonstrate that,in the scenario of low signal-to-noise ratio and a higher number of overlapping events,the fused auditory features exhibit superior robustness and multi-sound event detection performance compared to individual features such as NAP,MFCC,and GFCC.
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