On representation learning for artificial bandwidth extension

Matthias Zöhrer, Robert Peharz, Franz Pernkopf

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

3 Citations (Scopus)

Abstract

Recently, sum-product networks (SPNs) showed convincing results on the ill-posed task of artificial bandwidth extension (ABE). However, SPNs are just one type of many architectures which can be summarized as representational models. In this paper, using ABE as benchmark task, we perform a comparative study of Gauss Bernoulli restricted Boltzmann machines, conditional restricted Boltzmann machines, higher order contractive autoencoders, SPNs and generative stochastic networks (GSNs). Especially the latter ones are promising architectures in terms of its reconstruction capabilities. Our experiments show impressive results of GSNs, achieving on average an improvement of 3.90dB and 4.08dB in segmental SNR on a speaker dependent (SD) and speaker independent (SI) scenario compared to SPNs, respectively.

Original languageEnglish
Title of host publicationProceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
PublisherISCA
Pages791-795
Number of pages5
Publication statusPublished - 1 Jan 2015
Externally publishedYes
Event16th Annual Conference of the International Speech Communication Association, INTERSPEECH 2015 - Dresden, Germany
Duration: 6 Sep 201510 Sep 2015

Conference

Conference16th Annual Conference of the International Speech Communication Association, INTERSPEECH 2015
CountryGermany
CityDresden
Period6/09/1510/09/15

Keywords

  • Bandwidth extension
  • General stochastic network
  • Representation learning
  • Sum-product network

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