Variational Log-Power Spectral Tracking for Acoustic Signals

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/CongresprocedureConferentiebijdrageAcademicpeer review

Samenvatting

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.
Originele taal-2Engels
Titel2021 IEEE Statistical Signal Processing Workshop (SSP)
UitgeverijInstitute of Electrical and Electronics Engineers
Pagina's311-315
Aantal pagina's5
ISBN van elektronische versie978-1-7281-5767-2
ISBN van geprinte versie978-1-7281-5768-9
DOI's
StatusGepubliceerd - 19 aug 2021
Evenement2021 IEEE Statistical Signal Processing Workshop (SSP) - Rio de Janeiro, Brazil
Duur: 11 jul 202114 jul 2021

Congres

Congres2021 IEEE Statistical Signal Processing Workshop (SSP)
Periode11/07/2114/07/21

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