ICA '09 Proceedings of the 8th International Conference on Independent Component Analysis and Signal Separation
Online blind source separation based on time-frequency sparseness
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
IEEE Transactions on Audio, Speech, and Language Processing
Adaptive segmentation and separation of determined convolutive mixtures under dynamic conditions
LVA/ICA'10 Proceedings of the 9th international conference on Latent variable analysis and signal separation
The 2010 signal separation evaluation campaign (SiSEC2010): audio source separation
LVA/ICA'10 Proceedings of the 9th international conference on Latent variable analysis and signal separation
The 2011 signal separation evaluation campaign (SiSEC2011): - audio source separation -
LVA/ICA'12 Proceedings of the 10th international conference on Latent Variable Analysis and Signal Separation
Tracking of multidimensional TDOA for multiple sources with distributed microphone pairs
Computer Speech and Language
Online blind speech separation using multiple acoustic speaker tracking and time-frequency masking
Computer Speech and Language
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In this paper, we propose a novel method for blind source separation (BSS) based on time-frequency sparseness (TF) that can estimate the number of sources and time-frequency masks, even if the spatial aliasing problem exists. Many previous approaches, such as degenerate unmixing estimation technique (DUET) or observation vector clustering (OVC), are limited to microphone arrays of small spatial extent to avoid spatial aliasing. We develop an offline and an online algorithm that can both deal with spatial aliasing by directly comparing observed and model phase differences using a distance metric that incorporates the phase indeterminacy of 2p and considering all frequency bins simultaneously. Separation is achieved using a linear blind beamformer approach, hence musical noise common to binary masking is avoided. Furthermore, the offline algorithm can estimate the number of sources. Both algorithms are evaluated in simulations and real-world scenarios and show good separation performance.