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Testing for Local Stationarity in Acoustic Signals: Parametric and Nonparametric Approaches

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If you have a question about this talk, please contact Rachel Fogg .

This talk treats nonstationarity detection in the context of speech and audio time series, with broad application to stochastic processes exhibiting locally stationary behavior. Many such processes, in particular information-carrying natural sound signals, exhibit a degree of controlled nonstationarity that varies slowly over time. The talk first describes the basic concepts of these systems and their analysis via local Fourier methods. Parametric approaches appropriate for speech are then introduced by way of time-varying autoregressions, along with nonparametric approaches based on time-localized power spectral density estimates, along with an efficient offline bootstrap procedure based on the Wold representation. The talk includes asymptotic results as well as practical examples and applications in speech forensics and audio waveform segmentation.

This talk is part of the Probabilistic Systems, Information, and Inference Group Seminars series.

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