Principal subspace modification for multi-channel Wiener filter in multi-microphone noise reduction
In multi-microphone noise reduction for single desired speech signal, the principal subspace based multi-channel Wiener filter provides better performance compared with the conventional multi-channel Wiener filter. The principal subspace vector estimates the acoustic transfer function vector up to a scaling factor. However, as input SNR becomes lower, the error increases in the acoustic transfer function vector estimation. In this paper, we propose the principal subspace modification which is controlled by the angle between the principal subspace vector and the steering vector of the desired speech signal. In the simulation, the proposed method is evaluated with multi-channel speech data which are degraded by interfering noise coming from other direction. The simulation results show that the modification of principal subspace vector allows better performance compared to the conventional principal subspace based multichannel Wiener filter.