Blind separation of disjoint orthogonal signals: demixing N sources from 2 mixtures
ICASSP '00 Proceedings of the Acoustics, Speech, and Signal Processing, 2000. on IEEE International Conference - Volume 05
Blind separation of speech mixtures via time-frequency masking
IEEE Transactions on Signal Processing
Performance measurement in blind audio source separation
IEEE Transactions on Audio, Speech, and Language Processing
Mask estimation for missing data speech recognition based on statistics of binaural interaction
IEEE Transactions on Audio, Speech, and Language Processing
Self-localizing dynamic microphone arrays
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Evaluating source separation algorithms with reverberant speech
IEEE Transactions on Audio, Speech, and Language Processing - Special issue on processing reverberant speech: methodologies and applications
The cocktail party robot: sound source separation and localisation with an active binaural head
HRI '12 Proceedings of the seventh annual ACM/IEEE international conference on Human-Robot Interaction
A latently constrained mixture model for audio source separation and localization
LVA/ICA'12 Proceedings of the 10th international conference on Latent Variable Analysis and Signal Separation
Online blind speech separation using multiple acoustic speaker tracking and time-frequency masking
Computer Speech and Language
Modulation domain blind speech separation in noisy environments
Speech Communication
Bayesian Nonparametrics for Microphone Array Processing
IEEE/ACM Transactions on Audio, Speech and Language Processing (TASLP)
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This paper describes a system, referred to as model-based 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 maximum-likelihood 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 spectrogram masks that can be used for source separation. In simulated anechoic and reverberant environments, separations using MESSL produced on average a signal-todistortion ratio 1.6 dB greater and Perceptual Evaluation of Speech Quality (PESQ) results 0.27 mean opinion score units greater than four comparable algorithms.