Online Adaptation for Jointly Optimized Blind Source Separation and Dereverberation of Speech Mixtures
Konferenz: Speech Communication - 13. ITG-Fachtagung Sprachkommunikation
10.10.2018 - 12.10.2018 in Oldenburg, Deutschland
Tagungsband: ITG-Fb. 282: Speech Communication
Seiten: 5Sprache: EnglischTyp: PDFPersönliche VDE-Mitglieder erhalten auf diesen Artikel 10% Rabatt
Schuster, Timo; Feldes, Stefan (Institute of Digital Signal Processing, Mannheim University of Applied Sciences, Mannheim, Germany)
Blind source separation (BSS) and blind dereverberation (BD) are known to improve a desired speech signal when it is degraded by concurrent sound sources and reverberant environments, respectively. However, the BSS performance suffers from strong reverberation and BD usually suffers when there are multiple sound sources active. Thus, it has been proposed to connect both methods in tandem, so that they mutually profit from their respective gains. These previously presented schemes, however, work in batch mode preventing their direct use in real-time applications. In this paper a RLS-based method for adaptively and jointly performing BD and BSS of speech mixtures is proposed. It runs in fully online mode and can adapt to abrupt changes of target speaker positions. Experimental results show improvements in signal-to-interference ratio by an average of about 2dB compared to a sequential use of BD and BSS, as well as improvements in direct-toreverberant ratio.