Probabilistic amplitude demodulation

Authors:
Richard E. Turner;Maneesh Sahani
Affiliations:
Gatsby Computational Neuroscience Unit, UCL, London, UK;Gatsby Computational Neuroscience Unit, UCL, London, UK
Venue:
ICA'07 Proceedings of the 7th international conference on Independent component analysis and signal separation
Year:
2007

Citing 3
Cited 1

Time-frequency analysis: theory and applications

Time-frequency analysis: theory and applications
Information Theory, Inference & Learning Algorithms

Information Theory, Inference & Learning Algorithms
Probabilistic Non-linear Principal Component Analysis with Gaussian Process Latent Variable Models

The Journal of Machine Learning Research

Temporal contrast normalization and edge-preserved smoothing of temporal modulation structures of speech for robust speech recognition

Speech Communication

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Abstract

Auditory scene analysis is extremely challenging. One approach, perhaps that adopted by the brain, is to shape useful representations of sounds on prior knowledge about their statistical structure. For example, sounds with harmonic sections are common and so time-frequency representations are efficient. Most current representations concentrate on the shorter components. Here, we propose representations for structures on longer time-scales, like the phonemes and sentences of speech. We decompose a sound into a product of processes, each with its own characteristic time-scale. This demodulation cascade relates to classical amplitude demodulation, but traditional algorithms fail to realise the representation fully. A new approach, probabilistic amplitude demodulation, is shown to out-perform the established methods, and to easily extend to representation of a full demodulation cascade.