Natural gradient works efficiently in learning
Neural Computation
On-line Convolutive Blind Source Separation of Non-Stationary Signals
Journal of VLSI Signal Processing Systems
Space-Time Processing for Wireless Communications
ICASSP '97 Proceedings of the 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '97) -Volume 1 - Volume 1
Blind Separation of Multiple Speakers in a Multipath Environment
ICASSP '97 Proceedings of the 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '97) -Volume 1 - Volume 1
Nonholonomic Orthogonal Learning Algorithms for Blind Source Separation
Neural Computation
Signal separation by symmetric adaptive decorrelation: stability,convergence, and uniqueness
IEEE Transactions on Signal Processing
Time-domain convolutive blind source separation employing selective-tap adaptive algorithms
EURASIP Journal on Audio, Speech, and Music Processing
EURASIP Journal on Applied Signal Processing
Empirical methods to determine the number of sources in single-channel musical signals
IEEE Transactions on Audio, Speech, and Language Processing
Speech dereverberation based on variance-normalized delayed linear prediction
IEEE Transactions on Audio, Speech, and Language Processing - Special issue on processing reverberant speech: methodologies and applications
EURASIP Journal on Audio, Speech, and Music Processing - Special issue on environmental sound synthesis, processing, and retrieval
A multistage approach to blind separation of convolutive speech mixtures
Speech Communication
ISNN'05 Proceedings of the Second international conference on Advances in neural networks - Volume Part II
Journal of Signal Processing Systems
Modulation domain blind speech separation in noisy environments
Speech Communication
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Convolutive blind separation of speech, also known as the "cocktail party problem", is a challenging task for which few successful algorithms have been developed. In this paper, we explore two novel algorithms for separating mixtures of multiple speech signals as measured by multiple microphones in a room environment. Both algorithms are modifications of an existing approach for density-based multichannel blind deconvolution (MBD) using natural gradient adaptation. The first approach employs non-holonomic constraints on the multichannel separation system to effectively avoid the partial deconvolution of the extracted speech signals within the separation system's outputs. The second approach employs linear predictors within the coefficient updates and produces separated speech signals whose auto-correlation properties can be arbitrarily specified. Unlike MBD methods, the proposed techniques maintain the spectral content of the original speech signals in the extracted outputs. Performance comparisons of the proposed methods with existing techniques show their usefulness in separating real-world speech signal mixtures.