Self-Adaption in Single-Channel Source Separation

Single-channel source separation (SCSS) usually uses pre-trained source-specific models to separate the sources. These models capture the characteristics of each source and they perform well when matching the test conditions. In this paper, we extend the applicability of SCSS. We develop an EM-like iterative adaption algorithm which is capable to adapt the pre-trained models to the changed characteristics of the specific situation, such as a different acoustic channel introduced by variation in the room acoustics or changed speaker position. The adaption framework requires signal mixtures only, i.e. specific single source signals are not necessary. We consider speech/noise mixtures and we restrict the adaption to the speech model only. Model adaption is empirically evaluated using mixture utterances from the CHiME 2 challenge. We perform experiments using speaker dependent (SD) and speaker independent (SI) models trained on clean or reverberated single speaker utterances. We successfully adapt SI source models trained on clean utterances and achieve almost the same performance level as SD models trained on reverberated utterances.