Deep Convolution Blind Separation of Acoustic Signals Based on Joint Diagonalization

Abstract

The propagation of acoustic signal in space has a strong multipath effect, and the receiver often overlaps in the form of convolution. Especially in strong reverberation conditions such as ocean and theatre, where the length of impulse response of hybrid filter increases significantly. In order to eliminate the problem that long impulse response leads to the failure of the frequency domain convolution blind separation algorithm, two Short-Time Fourier Transforms (STFT) are applied to the observed signal. The first STFT shortens the length of the hybrid filter. The second STFT converts the signal model into instantaneous blind separation. Finally, the separation matrix is estimated by Joint Diagonalization (JD) technique. Compared with the existing methods, this method solves the problem of model failure under deep convolution mixing, and can obtain better separation performance when the number of source signals is large or additive noise exists. The simulation results verify the effectiveness and performance advantages of the proposed method.