Model-Based Expectation-Maximization Source Separation and Localization

Abstract—This paper describes a system, referred to as modelbased expectation-maximization source separation and localization (MESSL), for separating and localizing multiple sound sources from an underdetermined reverberant two-channel recording. By clustering individual spectrogram points based on their interaural phase and level differences, MESSL generates masks that can be used to isolate individual sound sources. We first describe a probabilistic model of interaural parameters that can be evaluated at individual spectrogram points. By creating a mixture of these models over sources and delays, the multi-source localization problem is reduced to a collection of single source problems. We derive an expectation-maximization algorithm for computing the maximumlikelihood parameters of this mixture model, and show that these parameters correspond well with interaural parameters measured in isolation. As a byproduct of fitting this mixture model, the algorithm creates probabilistic s...

• Type: Journal
• Year: 2010
• Where: TASLP
• Authors: Michael I. Mandel, Ron J. Weiss, Daniel P. W. Ellis
• Keywords: Individual Spectrogram, Sound Sources, Source Separation, TASLP 2010