Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
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
EURASIP Journal on Applied Signal Processing
Blind separation of speech mixtures via time-frequency masking
IEEE Transactions on Signal Processing
Blind source separation with dynamic source number using adaptive neural algorithm
Expert Systems with Applications: An International Journal
ICA '09 Proceedings of the 8th International Conference on Independent Component Analysis and Signal Separation
Indeterminacy free frequency-domain blind separation of reverberant audio sources
IEEE Transactions on Audio, Speech, and Language Processing
Determining mixing parameters from multispeaker data using speech-specific information
IEEE Transactions on Audio, Speech, and Language Processing
GLOBECOM'09 Proceedings of the 28th IEEE conference on Global telecommunications
Underdetermined convolutive blind source separation via time-frequency masking
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
A multistage approach to blind separation of convolutive speech mixtures
Speech Communication
Underdetermined DOA estimation via independent component analysis and time-frequency masking
Journal of Electrical and Computer Engineering
DOA Estimation for Multiple Sparse Sources with Arbitrarily Arranged Multiple Sensors
Journal of Signal Processing Systems
Interactive music with active audio CDs
CMMR'10 Proceedings of the 7th international conference on Exploring music contents
Noise variance estimation based on dual-channel phase difference for speech enhancement
Digital Signal Processing
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This paper presents a new method for blind sparse source separation. Some sparse source separation methods, which rely on source sparseness and an anechoic mixing model, have already been proposed. These methods utilize level ratios and phase differences between sensor observations as their features, and they separate signals by classifying them. However, some of the features cannot form clusters with a well-known clustering algorithm, e.g., the k-means. Moreover, most previous methods utilize a linear sensor array (or only two sensors), and therefore they cannot separate symmetrically positioned sources. To overcome such problems, we propose a new feature that can be clustered by the k-means algorithm and that can be easily applied to more than three sensors arranged non-linearly. We have obtained promising results for two- and three-dimensionally distributed speech separation with non-linear/non-uniform sensor arrays in a real room even in underdetermined situations. We also investigate the way in which the performance of such methods is affected by room reverberation, which may cause the sparseness and anechoic assumptions to collapse.