Integrating noise estimation and factorization-based speech separation: A novel hybrid approach

We present a novel method to integrate noise estimates by unsupervised speech enhancement algorithms into a semi-supervised non-negative matrix factorization framework. A multiplicative update algorithm is derived to estimate a non-negative noise dictionary given a time-varying background noise estimate with a stationarity constraint. A large-scale, speaker-independent evaluation is carried out on spontaneous speech overlaid with the official CHiME 2011 Challenge corpus of realistic domestic noise, as well as music and stationary environmental noise corpora. In the result, the proposed method delivers higher signal-distortion ratio and objective perceptual measure than standard semi-supervised NMF or spectral subtraction based on the same noise estimation algorithm, and further gains can be expected by speaker adaptation.