Reliable A posteriori Signal-to-Noise Ratio features selection

Abstract : This paper adresses the problem of single microphone speech enhancement in noisy environments. State of the art short-time noise reduction techniques are most often expressed as a spectral gain depending on the Signal-to-Noise Ratio (SNR). The well-known decision-directed (DD) approcah drastically limits the level of musical noise but the estimated a priori SNR is biased since it depends on the speech spectrum estimated in the previous frame. The consequence of this biais is an annoying reverberation effect. We propose a new method, called Reliable Features Selection Noise Reduction (RFSNR) technique, that is able to classify the a posteriori SNR estimates into two categories: the reliable features leading to speech components and the unrealiable ones corresponding to musical noise only. Then it is possible to directly enhance speech using a posteriori SNR leading to an unbiased estimator.