A wavelet transform approach to blind adaptive filtering of speech from unknown noises

This paper is concerned with the application of wavelet transforms (WT) to blind adaptive filtering (BAF) of speech signals from unknown colored noise under a single-microphone constraint when neither speech nor noise are separately accessible. The paper describes the BAF system which performs discrete WT of noisy speech signals and identifies and separates noise from speech. The BAF then tunes sets of weights that multiply WT coefficients on different scales of the WT to produce a significantly improved signal-to-noise ratio and intelligibility of the filtered speech signal at the output of the BAF.