Realtime multiple-pitch and multiple-instrument recognition for music signals using sparse non-negative constraints

Arshia Cont, Shlomo Dubnov, David Wessel

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

23 Scopus citations

Abstract

In this paper we introduce a simple and fast method for realtime recognition of multiple pitches produced by multiple musical instruments. Our proposed method is based on two important facts: (1) that timbral information of any instrument is pitch-dependant and (2) that the modulation spectrum of the same pitch seems to result into a persistent representation of the characteristics of the instrumental family. Using these basic facts, we construct a learning algorithm to obtain pitch templates of all possible notes on various instruments and then devise an online algorithm to decompose a realtime audio buffer using the learned templates. The learning and decomposition proposed here are inspired by non-negative matrix factorization methods but differ by introduction of an explicit sparsity control. Our test results show promising recognition rates for a realtime system on real music recordings. We discuss further improvements that can be made over the proposed system.

Original languageEnglish
Title of host publicationProceedings of the 10th International Conference on Digital Audio Effects, DAFx 2007
Pages85-92
Number of pages8
StatePublished - 1 Dec 2007
Externally publishedYes
Event10th International Conference on Digital Audio Effects, DAFx 2007 - Bordeaux, France
Duration: 10 Sep 200715 Sep 2007

Publication series

NameProceedings of the 10th International Conference on Digital Audio Effects, DAFx 2007

Conference

Conference10th International Conference on Digital Audio Effects, DAFx 2007
Country/TerritoryFrance
CityBordeaux
Period10/09/0715/09/07

ASJC Scopus subject areas

  • Signal Processing

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