Blind separation of disjoint orthogonal signals: demixing N sources from 2 mixtures

By A. Jourjine, Scott T. Rickard, Özgür Yilmaz. 2000 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.00CH37100), 2000

487 citations recorded by the source corpus.

Subfields: Computational Mechanics, Signal Processing

View Paper: https://www.semanticscholar.org/paper/cd8864a740b2c8ad4c949137562f99cbedb27250

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Citations in the corpus

A time-frequency blind signal separation method applicable to underdetermined mixtures of dependent sources (2005, methodology): “A TF method called DUET was also developed for delay and attenuation mixtures [14] and tested with convolutive mixtures, using real-time computation [15].”
Direction of Arrival Based Spatial Covariance Model for Blind Sound Source Separation (2014, unknown): “Separation methods directly utilizing TDoAs between microphones and creating time-frequency separation masks by clustering the measured TDoAs at each frequency include for example DUET [10] and binwise clustering [11].”
Performance measurement in blind audio source separation (2006, methodology): “Two algorithms are tested: a clustering of the short-time Fourier transform (STFT) called DUET [12] (using a 256-sample Hanning window and a 192-sample hopsize for STFT computation) and a matching pursuit clustering (MPC) [11] (using 10 000 Gabor atoms with truncated Gaussian envelope).”
A Consolidated Perspective on Multimicrophone Speech Enhancement and Source Separation (2017, unknown): “Spectral information relating to the source time-frequency characteristics is modeled for example by constraining the source spectrograms to be low-rank, sparse, and/or to have an excitation-ﬁlter structure.”
Blind separation of speech mixtures via time-frequency masking (2004, methodology): “The mixing model in [1]–[3], [5], [8], [9], and [11] is " instantaneous " (sources have different amplifications in different mixtures), whereas [4], [6], [7], [10], and [12] use an anechoic mixing model (sources have different amplifications and time delays in different mixtures).”
Model-Based Expectation-Maximization Source Separation and Localization (2010, methodology): “The simplification made by DUET [4], [6] is to ignore high frequencies in which the cross-correlation is multimodal and to convert interaural phase”
On Ideal Binary Mask As the Computational Goal of Auditory Scene Analysis (2005, background): “Related to binary masks is the observation that different speech utterances tend to be orthogonal in a high-resolution time-frequency representation because the energy of a single utterance tends to be sparsely distributed (Jourjine et al., 2000; Roweis, 2001).”
Sparse component analysis and blind source separation of underdetermined mixtures (2005, background): “In [ 6 ], theauthors supposethat thesupports of theFourie r transform of any two source signals are disjoint sets—a much more restrictive condition than our condition.”
Underdetermined Convolutive Blind Source Separation via Frequency Bin-Wise Clustering and Permutation Alignment (2011, methodology): “F) masking [14]–[23] or a maximum a posteriori (MAP) estimator [24]–[27] is widely used to separate such underdetermined mixtures.”
Handbook of Blind Source Separation: Independent Component Analysis and Applications (2010, background): “Some ten years elapsed before these techniques began to be fully exploited for blind source separation [72, 106, 66, 70, 116, 61, 15, 111].”