WIRE3: Driving Around the Information Super-Highway
Personal and Ubiquitous Computing
Learning Spectral Clustering, With Application To Speech Separation
The Journal of Machine Learning Research
Variational and stochastic inference for Bayesian source separation
Digital Signal Processing
Speech source separation in convolutive environments using space-time-frequency analysis
EURASIP Journal on Applied Signal Processing
3D-audio matting, postediting, and rerendering from field recordings
EURASIP Journal on Applied Signal Processing
ESPOCO'05 Proceedings of the 4th WSEAS International Conference on Electronic, Signal Processing and Control
Stereo audio source separation based on time--frequency masking and multilevel thresholding
Digital Signal Processing
K-hyperline clustering learning for sparse component analysis
Signal Processing
A time-frequency blind source separation method based on segmented coherence function
IWANN '03 Proceedings of the 7th International Work-Conference on Artificial and Natural Neural Networks: Part II: Artificial Neural Nets Problem Solving Methods
Model-based expectation-maximization source separation and localization
IEEE Transactions on Audio, Speech, and Language Processing
Evaluating source separation algorithms with reverberant speech
IEEE Transactions on Audio, Speech, and Language Processing - Special issue on processing reverberant speech: methodologies and applications
Blind music timbre source isolation by multi- resolution comparison of spectrum signatures
RSCTC'10 Proceedings of the 7th international conference on Rough sets and current trends in computing
A sparsity-based approach to 3D binaural sound synthesis using time-frequency array processing
EURASIP Journal on Advances in Signal Processing - Special issue on digital audio effects
Double sparsity: towards blind estimation of multiple channels
LVA/ICA'10 Proceedings of the 9th international conference on Latent variable analysis and signal separation
Sparse source separation of non-instantaneous spatially varying single path mixtures
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part IV
BLUES from music: BLind underdetermined extraction of sources from music
ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation
Convolutive demixing with sparse discrete prior models for markov sources
ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation
Blind separation of sparse sources using jeffrey’s inverse prior and the EM algorithm
ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation
Separating underdetermined convolutive speech mixtures
ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation
Two time-frequency ratio-based blind source separation methods for time-delayed mixtures
ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation
Signal sparsity enhancement through wavelet transforms in underdetermined BSS
Nonlinear Speech Modeling and Applications
Low-Latency instrument separation in polyphonic audio using timbre models
LVA/ICA'12 Proceedings of the 10th international conference on Latent Variable Analysis and Signal Separation
Acoustic Rendering and Auditory–Visual Cross-Modal Perception and Interaction
Computer Graphics Forum
Mixing matrix estimation using discriminative clustering for blind source separation
Digital Signal Processing
Modulation domain blind speech separation in noisy environments
Speech Communication
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We present a novel method for blind separation of any number of sources using only two mixtures. The method applies when sources are (W-)disjoint orthogonal, that is, when the supports of the (windowed) Fourier transform of any two signals in the mixture are disjoint sets. We show that, for anechoic mixtures of attenuated and delayed sources, the method allows one to estimate the mixing parameters by clustering ratios of the time-frequency representations of the mixtures. The estimates of the mixing parameters are then used to partition the time-frequency representation of one mixture to recover the original sources. The technique is valid even in the case when the number of sources is larger than the number of mixtures. The general results are verified on both speech and wireless signals.