Polyphonic pitch tracking by example

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

Abstract

We introduce a novel approach for pitch tracking of multiple sources in mixture signals. Unlike traditional approaches to pitch tracking, which explicitly attempt to detect periodicities, this approach is using a learning framework by making use of previously pitch-tagged recordings as training data to teach spectrum/pitch associations. We show how the mixture case of this task is a nearest subspace search problem which is efficiently solved by transforming it to an overcomplete sparse coding formulation. We demonstrate the use of this algorithm with real mixtures ranging from solo up to a quintet recordings.

Original languageEnglish (US)
Title of host publication2011 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, WASPAA 2011
Pages125-128
Number of pages4
DOIs
StatePublished - Dec 19 2011
Event2011 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, WASPAA 2011 - New Paltz, NY, United States
Duration: Oct 16 2011Oct 19 2011

Publication series

NameIEEE Workshop on Applications of Signal Processing to Audio and Acoustics

Other

Other2011 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, WASPAA 2011
Country/TerritoryUnited States
CityNew Paltz, NY
Period10/16/1110/19/11

Keywords

  • Polyphonic pitch tracking

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Computer Science Applications

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