Independent component analysis, a new concept?
Signal Processing - Special issue on higher order statistics
Recent Research Towards Advanced Man-Machine Interface through Spoken Language
Recent Research Towards Advanced Man-Machine Interface through Spoken Language
Advances in Independent Component Analysis
Advances in Independent Component Analysis
Self-Organizing Maps
Co-Channel Speech Separation for Robust Automatic Speech Recognition: Stability and Efficiency
ICASSP '97 Proceedings of the 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '97)-Volume 2 - Volume 2
Separation of speech from interfering sounds based on oscillatory correlation
IEEE Transactions on Neural Networks
Computational models for neuroscience
Neural Networks - 2003 Special issue: Neural network analysis of complex scientific data: Astronomy and geosciences
Neural Computation
On noise masking for automatic missing data speech recognition: A survey and discussion
Computer Speech and Language
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We present a new approach to the cocktail party problem that uses a cortronic artificial neural network architecture (Hecht-Nielsen, 1998) as the front end of a speech processing system. Our approach is novel in three important respects. First, our method assumes and exploits detailed knowledge of the signals we wish to attend to in the cocktail party environment. Second, our goal is to provide preprocessing in advance of a pattern recognition system rather than to separate one or more of the mixed sources explicitly. Third, the neural network model we employ is more biologically feasible than are most other approaches to the cocktail party problem. Although the focus here is on the cocktail party problem, the method presented in this study can be applied to other areas of information processing.