Study of widely linear multichannel wiener filter for binaural noise reduction
Xin Leng, Jingdong Chen, Israel Cohen, Jacob Benesty · 2017
In this paper, we study the binaural noise-reduction problem using an array of microphones. The widely linear (WL) framework in the short-time-Fourier-transform (STFT) domain is adopted. In such a framework, the microphone array signals and binaural outputs are first merged into complex signals. These complex signals are subsequently transformed into the STFT domain. The WL estimation theory is then applied in STFT subbands with interband correlation to form the optimal WL Wiener filter, which exploits the noncircular properties of the input complex signals to achieve noise reduction and meanwhile to preserve the sound spatial realism. Finally, the time-domain binaural output is reconstructed from the output of the WL Wiener filter using the inverse STFT. The effectiveness of the developed STFT-domain WL Wiener filter for binaural noise reduction is justified using experiments.