Dictionary-Based Sparse Reconstruction of Incomplete Relative Transfer Functions

Zbyněk Koldovský, Sharon Gannot

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

1 Scopus citations

Abstract

For estimating the relative transfer function (RTF) of a speaker from noisy multi-microphone recordings, several statistical methods have been proposed. The estimation accuracy is different over frequencies, which mostly depends on the frequency-dependent signal-to-noise ratio (SNR). Provided that the low-SNR frequencies are identified, the corresponding values of the estimated RTF can be replaced through interpolation using the frequencies with high SNR. In this study, we explore interpolation techniques based on the sparse reconstruction of an incomplete RTF which is obtained when low-SNR values are neglected. Compared to previous attempts where the approximate sparsity of the time-domain representation of RTF (relative impulse response) is exploited, in this paper, we use learned sparse dictionaries trained on dense measurements of RTFs within a confined area of the target speaker. These measurements are obtained from the recently released MIRaGe database acquired in a real room.

Original languageEnglish
Title of host publication29th European Signal Processing Conference, EUSIPCO 2021 - Proceedings
PublisherEuropean Signal Processing Conference, EUSIPCO
Pages1005-1009
Number of pages5
ISBN (Electronic)9789082797060
DOIs
StatePublished - 2021
Event29th European Signal Processing Conference, EUSIPCO 2021 - Dublin, Ireland
Duration: 23 Aug 202127 Aug 2021

Publication series

NameEuropean Signal Processing Conference
Volume2021-August
ISSN (Print)2219-5491

Conference

Conference29th European Signal Processing Conference, EUSIPCO 2021
Country/TerritoryIreland
CityDublin
Period23/08/2127/08/21

Bibliographical note

Funding Information:
This work was supported by The Czech Science Foundation through Project No. 20-17720S.

Publisher Copyright:
© 2021 European Signal Processing Conference. All rights reserved.

Keywords

  • Dictionary learning
  • Relative transfer function
  • Room impulse responses
  • Sparse dictionaries
  • Sparse representations

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