End-to-End Probabilistic Inference for Nonstationary Audio Analysis

William Wilkinson, Michael Andersen, Joshua D. Reiss, Dan Stowell, Arno Solin

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

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Abstract

A typical audio signal processing pipeline includes multiple disjoint analysis stages, including calculation of a time-frequency representation followed by spectrogram-based feature analysis. We show how time-frequency analysis and nonnegative matrix factorisation can be jointly formulated as a spectral mixture Gaussian process model with nonstationary priors over the amplitude variance parameters. Further, we formulate this nonlinear model’s state space representation, making it amenable to infinite-horizon Gaussian process regression with approximate inference via expectation propagation, which scales linearly in the number of time steps and quadratically in the state dimensionality. By doing so, we are able to process audio signals with hundreds of thousands of data points. We demonstrate, on various tasks with empirical data, how this inference scheme outperforms more standard techniques that rely on extended Kalman filtering.
Original languageEnglish
Title of host publication36th International Conference on Machine Learning, ICML 2019
Pages6776–6785
ISBN (Electronic)9781510886988
Publication statusPublished - 2019
MoE publication typeA4 Article in a conference publication
EventInternational Conference on Machine Learning - Long Beach, United States
Duration: 9 Jun 201915 Jun 2019
Conference number: 36

Publication series

NameProceedings of Machine Learning Research
PublisherJMLR
Volume97
ISSN (Print)2640-3498

Conference

ConferenceInternational Conference on Machine Learning
Abbreviated titleICML
CountryUnited States
CityLong Beach
Period09/06/201915/06/2019

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