Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
Analysis of sparse representation and blind source separation
Neural Computation
Unitary ESPRIT: how to obtain increased estimation accuracy with areduced computational burden
IEEE Transactions on Signal Processing
Blind separation of speech mixtures via time-frequency masking
IEEE Transactions on Signal Processing
Underdetermined blind source separation based on sparse representation
IEEE Transactions on Signal Processing
Sparse component analysis and blind source separation of underdetermined mixtures
IEEE Transactions on Neural Networks
Advanced narrow speech channeling algorithm for robot speech recognition
Proceedings of the 2009 International Conference on Hybrid Information Technology
Indeterminacy free frequency-domain blind separation of reverberant audio sources
IEEE Transactions on Audio, Speech, and Language Processing
| Hi-index | 0.00 |
The DUET blind source separation algorithm can demix an arbitrary number of speech signals using M = 2 anechoic mixtures of the signals. DUET however is limited in that it relies upon source signals which are mixed in an anechoic environment and which are sufficiently sparse such that it is assumed that only one source is active at a given time frequency point. The DUET-ESPRIT (DESPRIT) blind source separation algorithm extends DUET to situations where M ≥ 2 sparsely echoic mixtures of an arbitrary number of sources overlap in time frequency. This paper outlines the development of the DESPRIT method and demonstrates its properties through various experiments conducted on synthetic and real world mixtures.