Improving independent vector analysis in speech and noise separation tasks

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Journal ISSN
Volume Title
Sähkötekniikan korkeakoulu | Master's thesis
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Author
Date
2015-05-11
Department
Major/Subject
Signal Processing
Mcode
S3013
Degree programme
TLT - Master’s Programme in Communications Engineering
Language
en
Pages
6+46
Series
Abstract
Independent vector analysis (IVA) is an efficient multichannel blind source separation method. However, source models conventionally assumed in IVA present some limitations in case of speech and noise separation tasks. Consequently, it is expected that using better source models that overcome these limitations will improve the source separation performance of IVA. In this work, an extension of IVA is proposed, with a new source model more suitable for speech and noise separation tasks. The proposed extended IVA was evaluated in a speech and noise separation task, where it was proven to improve separation performance over baseline IVA. Furthermore, extended IVA was evaluated with several post-filters, aiming to realize an analogous setup to a multichannel Wiener filter (MWF) system. This kind of setup proved to further increase the separation performance of IVA.
Description
Supervisor
Palomäki, Kalle
Thesis advisor
Ono, Nobutaka
Remes, Ulpu
Keywords
independent vector analysis, blind source separation, microphone array, speech source model, speech enhancement
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Permanent link to this item
https://urn.fi/URN:NBN:fi:aalto-201505283003
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[dipl] Sähkötekniikan korkeakoulu / ELEC