Publication
Title
Improving PLCA-based score-informed source separation with invertible constant-Q transforms
Author
Abstract
Probabilistic Latent Component Analysis is a widely adopted variant of Nonnegative Matrix Factorization for the purpose of single channel audio source separation. It has seen many extensions, including incorporation of prior information derived from music scores. Recent work on the invertibility of the Constant-Q Tranform make that a viable alternative to the Short-time Fourier Transform as underlying data representation. In this paper we assess several implementations for their usability in score-informed source separation. We show that results are comparable to, and in some cases better than, use of the STFT, and that exact transform invertibility is not a significant factor in this application.
Language
English
Source (journal)
Signal Processing Conference (EUSIPCO), European
Source (book)
EUSIPCO 2012, 20th European Signal Processing Conference, Bucharest, Romania, August 27-31
Publication
S.l. : IEEE, 2012
ISBN
978-1-4673-1068-0
Volume/pages
p. 2634-2638
ISI
000310623800530
UAntwerpen
Faculty/Department
Research group
Publication type
Subject
Affiliation
Publications with a UAntwerp address
External links
Web of Science
Record
Identification
Creation 28.11.2012
Last edited 11.08.2017
To cite this reference