Harmonic/percussive separation using Kernel Additive Modelling

Derry FitzGerald, Antoine Liukus, Zafar Rafii, Bryan Pardo, Laurent Daudet

Research output: Chapter in Book/Report/Conference proceedingConference contribution

22 Scopus citations

Abstract

Recently, Kernel Additive Modelling was proposed as a new framework for performing sound source separation. Kernel Additive Modelling assumes that a source at some location can be estimated using its values at nearby locations where nearness is defined through a source-specific proximity kernel. Different proximity kernels can be used for different sources, which are then separated using an iterative kernel backfitting algorithm. These kernels can efficiently account for features such as continuity, stability in time or frequency and self-similarity. Here, we show that Kernel Additive Modelling can be used to generalise, extend and improve on a widely-used harmonic/percussive separation algorithm which attempts to separate pitched from percussive instruments.

Original languageEnglish (US)
Title of host publicationIET Conference Publications
PublisherInstitution of Engineering and Technology
Pages35-40
Number of pages6
EditionCP639
ISBN (Print)9781849199247
DOIs
StatePublished - 2014
Event25th IET Irish Signals and Systems Conference, ISSC 2014 and China-Ireland International Conference on Information and Communications Technologies, CIICT 2014 - Limerick, Ireland
Duration: Jun 26 2014Jun 27 2014

Publication series

NameIET Conference Publications
NumberCP639
Volume2014

Other

Other25th IET Irish Signals and Systems Conference, ISSC 2014 and China-Ireland International Conference on Information and Communications Technologies, CIICT 2014
Country/TerritoryIreland
CityLimerick
Period6/26/146/27/14

Keywords

  • Harmonic/percussive separation
  • Kernel Additive Modelling
  • Sound source separation

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

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