Variational Log-Power Spectral Tracking for Acoustic Signals

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Abstract

This paper proposes a generative hierarchical probabilistic model for acoustic signals where both the frequency decomposition and log-power spectrum appear as latent variables. In order to facilitate efficient inference, we represent the model in a factor graph that includes a probabilistic Fourier transform and a Gaussian scale model as modules. We derive novel ways of performing variational message passing-based inference in the Gaussian scale model. As a result, in this model a probabilistic representation of the log-power spectrum of an acoustic signal can be effectively inferred online. The proposed model may find applications as a front end wherever probabilistic log-power spectral features of a signal are needed. We validate the model and message passing-based inference methods by tracking the log-power spectrum of a speech signal.
Original languageEnglish
Title of host publication2021 IEEE Statistical Signal Processing Workshop (SSP)
PublisherInstitute of Electrical and Electronics Engineers
Pages311-315
Number of pages5
ISBN (Electronic)978-1-7281-5767-2
ISBN (Print)978-1-7281-5768-9
DOIs
Publication statusPublished - 19 Aug 2021
Event2021 IEEE Statistical Signal Processing Workshop (SSP) - Rio de Janeiro, Brazil
Duration: 11 Jul 202114 Jul 2021

Conference

Conference2021 IEEE Statistical Signal Processing Workshop (SSP)
Period11/07/2114/07/21

Keywords

  • Fourier transforms
  • Conferences
  • Signal processing algorithms
  • Signal processing
  • Probabilistic logic
  • Acoustics
  • Inference algorithms

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