Single channel speech enhancement in severe noise conditions

[Truncated abstract] SINGLE Channel Non Stationary Noise Speech Enhancement (SCNSNSE) algorithms can be used in many applications including enhancement of pre-recorded speech, hearing aids devices, speech recognition and telecommunication equipment. Many organizations such as medical, aviation and local or federal police are interested in having access to algorithms that can improve noisy speech signals. A combined set of existing and new algorithms were uniquely put together to produce a SCNSNSE system architecture. This novel SCNSNSE system architecture produces improved speech enhancement at low SNR (below 0 dB SNR). This SCNSNSE architecture contains novel algorithms at pre and post processing stages and enhances the speech signal which is contaminated with highly non-stationary noise. The SCNSNSE architecture consists of three major layers. Layer one consists of spectrum estimation (the preprocessing) of the SCNSNSE system architecture. At the spectrum estimation layer the concept of narrow band variation reduction is explained. The novel introduction of the Controlled Forward Moving Average (CFMA) algorithm greatly improves the reduction of narrow band variation. The CFMA is strategically placed in the SCNSNSE architecture to provide a better outcome. At the spectrum estimation layer a combination of existing algorithms cascaded with a new algorithm is applied as described below: 1. Discrete or Prolate Spheroidal Sequence (DPSS) multi-taper algorithm. 2. Controlled Forward Moving Average (CFMA) algorithm. 3. Stein’s Unbiased Risk Estimator (SURE) wavelet thresholding. The second layer consists of the noise estimation (the post processing) algorithms. During the post-processing the concept of wide band variation estimation, Frequency Threshold Mapping (FTM) and Multi-Channel Threshold Mapping (MCTM) are introduced...