Improving independent vector analysis in speech and noise separation tasks

Thumbnail Image

Files

master_Ramírez_López_Ana_2015.pdf (1.58 MB)

URL

Journal Title

Journal ISSN

Volume Title

Sähkötekniikan korkeakoulu | Master's thesis
Unless otherwise stated, all rights belong to the author. You may download, display and print this publication for Your own personal use. Commercial use is prohibited.

Authors

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

Other note

Citation

Permanent link to this item

https://urn.fi/URN:NBN:fi:aalto-201505283003

Collections

[dipl] Sähkötekniikan korkeakoulu / ELEC