Robust Blind Source Separation to Noisy Environment For Speech Recognition in Car

차량용 음성인식을 위한 주변잡음에 강건한 브라인드 음원분리

  • 김현태 (동의대학교 멀티미디어공학과) ;
  • 박장식 (동의과학대학 디지털정보전자과)
  • Published : 2006.12.28


The performance of blind source separation(BSS) using independent component analysis (ICA) declines significantly in a reverberant environment. A post-processing method proposed in this paper was designed to remove the residual component precisely. The proposed method used modified NLMS(normalized least mean square) filter in frequency domain, to estimate cross-talk path that causes residual cross-talk components. Residual cross-talk components in one channel is correspond to direct components in another channel. Therefore, we can estimate cross-talk path using another channel input signals from adaptive filter. Step size is normalized by input signal power in conventional NLMS filter, but it is normalized by sum of input signal power and error signal power in modified NLMS filter. By using this method, we can prevent misadjustment of filter weights. The estimated residual cross-talk components are subtracted by non-stationary spectral subtraction. The computer simulation results using speech signals show that the proposed method improves the noise reduction ratio(NRR) by approximately 3dB on conventional FDICA.


FDICA;Post-Processing;Modified NLMS;Cross-Talk Cancellation;Noise Reudction Ratio