E. Vincent - A general flexible probabilistic framework for audio source separation
Source separation aims to extract the signals of individual sound sources from a given signal. It is one of the hottest topics in audio signal processing, with applications ranging from speech enhancement and robust speech recognition to 3D upmixing and post-production of music. In this talk, we will present the general probabilistic variance modeling framework and discuss its advantages compared to earlier approaches such as ICA, SCA, GMM or NMF. We will show how cues as diverse as harmonicity, timbre, temporal fine structure, spatial location and spatial spread can be jointly exploited by means of hierarchical source models and probabilistic priors. We will illustrate the resulting separation quality via a number of sound examples from the Signal Separation Evaluation Campaign (SiSEC).