Adaptation of source-specific dictionaries in Non-Negative Matrix Factorization for source separation

This paper concerns the adaptation of spectrum dictionaries in audio source separation with supervised learning. Supposing that samples of the audio sources to separate are available, a filter adaptation in the frequency domain is proposed in the context of Non-Negative Matrix Factorization with the Itakura-Saito divergence. The algorithm is able to retrieve the acoustical filter applied to the sources with a good accuracy, and demonstrates significantly higher performances on separation tasks when compared with the non-adaptive model.