Nonlinear speech processing with oscillatory neural networks for speaker segregation

Nonlinear masking of space-time representations of speech is a universal technique for speech processing. In the present work we use an AM representation of cochlear filterbank signals in combination with a mask that is derived from a network of oscillatory neurons. The proposed approach does not need any training or learning and the mask takes into account the dependence between points from the auditory derived representation. A potential application is illustrated in the context of speaker segregation.

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