Julian Becker

Nonnegative Matrix Factorization with Adaptive Elements for Monaural Audio Source Separation


Front	Back

ISBN:

978-3-8440-4814-8

Series:

Aachen Series on Multimedia and Communications Engineering
Herausgeber: Univ.-Prof. Dr.-Ing. Jens-Rainer Ohm
Aachen

Volume:

Keywords:

Audio Source Separation; Nonnegative Matrix Factorization

Type of publication:

Thesis

Language:

English

Pages:

152 pages

Figures:

85 figures

Weight:

207 g

Format:

21 x 14,8 cm

Bindung:

Paperback

Price:

45,80 € / 57,30 SFr

Published:

October 2016

Buy:

Download:

Available PDF-Files for this title:

You need the Adobe Reader, to open the files. Here you get help and information, for the download.

These files are not printable.


	Document		Table of contents
	Type		PDF
	Costs		free
	Action		Display of file - 59 kB (60017 Byte)
	Action		Download of file - 59 kB (60017 Byte)


	Document		Document
	Type		PDF
	Costs		34,35 EUR
	Action		Purchase in obligation and display of file - 2,7 MB (2850203 Byte)
	Action		Purchase in obligation and download of file - 2,7 MB (2850203 Byte)

User settings for registered users

You can change your address here or download your paid documents again.

User:	Not logged in.
Actions:	Login / Register Forgotten your password?

Recommendation:

You want to recommend this title?

Review copy:

Here you can order a review copy.

Link:

You want to link this page? Click here.

Export citations:

Text
BibTex
RIS

Abstract:

For monaural audio source separation, Nonnegative Matrix Factorization (NMF) has become one of the most dominant methods. This thesis contributes adaptive methods in the context of two extensions: Nonnegative Matrix Factor Deconvolution (NMFD) and constraints for NMF computation. It is shown that these elements improve separation results only under specific conditions. Consequently, adaptive algorithms are developed in this thesis. The underlying hypothesis of these modifications is that NMF components have different properties depending on the acoustical event to which they correspond.
In the context of NMFD, a generalization of NMFD is proposed which makes it possible to use the deconvolution approach only on a subset of the components. Further, with this approach it is possible to adapt the parameters of NMFD on each component individually. Experimental results show that an algorithm using the generalized NMFD leads to better separation results as a comparable algorithm using NMF or NMFD.
For NMF with additional constraints, two adaptive extensions are presented. The first extension adapts the constraints depending on the properties of different NMF components. As a result, these constraints are imposed stronger on the components for which they are beneficial and weaker on others. Secondly, an algorithm is developed, which also makes it possible to adapt the strength of the constraints to different entire mixtures during runtime of the NMF. Experimental results show that both algorithms are beneficial for the source separation results.
The proposed adaptive elements for NMF prove to be an effective addition to the state of the art of NMF, enabling improved quality of fully automatic blind source separation of monaural audio mixtures.