Single channel speech enhancement using subband iterative Kalman filter
Sujan Kumar Roy, Wei‐Ping Zhu, Benoı̂t Champagne · 2016
In this paper, we propose a single channel speech enhancement algorithm using a subband iterative Kalman filter. A wavelet filterbank is first used to decompose the noise corrupted speech into a number of subbands. To achieve the best tradeoff among the noise reduction, speech intelligibility and computational complexity, a partial reconstruction scheme based on consecutive mean squared error is proposed to synthesize the low-frequency (LF) and high-frequency (HF) subbands. An iterative Kalman filter is then applied to the partially reconstructed HF subband speech. Finally, the enhanced HF subband speech is combined with the partially reconstructed LF subband speech to reconstruct the fullband enhanced speech. Experimental results show that the proposed subband iterative Kalman filter based algorithm is capable of reducing adverse environmental noises for a wide range of input SNRs. The overall performance of our method in terms of segmental SNR, perceptual evaluation of speech quality (PESQ) and computational cost is superior to several existing Kalman filter based algorithms.