Independent component analysis: algorithms and applications
Neural Networks
Rao-Blackwellized particle filter for multiple target tracking
Information Fusion
Robotics and Autonomous Systems
Evaluating multiple object tracking performance: the CLEAR MOT metrics
Journal on Image and Video Processing - Regular
Measurement combination for acoustic source localization in a room environment
EURASIP Journal on Audio, Speech, and Music Processing - Intelligent Audio, Speech, and Music Processing Applications
Sound Capture and Processing: Practical Approaches
Sound Capture and Processing: Practical Approaches
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
Online blind source separation based on time-frequency sparseness
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
Model-based expectation-maximization source separation and localization
IEEE Transactions on Audio, Speech, and Language Processing
Blind source separation based on time-frequency sparseness in the presence of spatial aliasing
LVA/ICA'10 Proceedings of the 9th international conference on Latent variable analysis and signal separation
Underdetermined DOA estimation via independent component analysis and time-frequency masking
Journal of Electrical and Computer Engineering
Blind separation of speech mixtures via time-frequency masking
IEEE Transactions on Signal Processing
A Bayesian approach to tracking multiple targets using sensorarrays and particle filters
IEEE Transactions on Signal Processing
Microphone Array Shape Calibration in Diffuse Noise Fields
IEEE Transactions on Audio, Speech, and Language Processing
Performance measurement in blind audio source separation
IEEE Transactions on Audio, Speech, and Language Processing
Speech Enhancement and Recognition in Meetings With an Audio–Visual Sensor Array
IEEE Transactions on Audio, Speech, and Language Processing
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Separating speech signals of multiple simultaneous talkers in a reverberant enclosure is known as the cocktail party problem. In real-time applications online solutions capable of separating the signals as they are observed are required in contrast to separating the signals offline after observation. Often a talker may move, which should also be considered by the separation system. This work proposes an online method for speaker detection, speaker direction tracking, and speech separation. The separation is based on multiple acoustic source tracking (MAST) using Bayesian filtering and time-frequency masking. Measurements from three room environments with varying amounts of reverberation using two different designs of microphone arrays are used to evaluate the capability of the method to separate up to four simultaneously active speakers. Separation of moving talkers is also considered. Results are compared to two reference methods: ideal binary masking (IBM) and oracle tracking (O-T). Simulations are used to evaluate the effect of number of microphones and their spacing.