In this letter, we present a speech enhancement technique based on the ambient noise classification incorporating the Gaussian mixture model (GMM). The principal parameters of the statistical model-based speech enhancement algorithm such as the weighting parameter in the decision-directed (DD) method and the long-term smoothing parameter of the noise estimation, are chosen as different values according to the classified contexts to ensure best performance for each noise. For the real-time environment awareness, the noise classification is performed on a frame-by-frame basis using the GMM with the soft decision framework. The speech absence probability (SAP) is used in detecting the speech absence periods and updating the likelihood of the GMM.
Bibliographic reference. Choi, Jae-Hun / Kim, Sang-Kyun / Chang, Joon-Hyuk (2011): "A soft decision-based speech enhancement using acoustic noise classification", In INTERSPEECH-2011, 1193-1196.