Bilevel sparse models for polyphonic music transcription

Tal Ben Yakar, Roee Litman, Pablo Sprechmann, Alex Bronstein, Guillermo Sapiro

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

Abstract

In this work, we propose a trainable sparse model for automatic polyphonic music transcription, which incorporates several successful approaches into a unified optimization framework. Our model combines unsupervised synthesis models similar to latent component analysis and nonnegative factorization with metric learning techniques that allow supervised discriminative learning. We develop efficient stochastic gradient training schemes allowing unsupervised, semi-, and fully supervised training of the model as well its adaptation to test data. We show efficient fixed complexity and latency approximation that can replace iterative minimization algorithms in time-critical applications. Experimental evaluation on synthetic and real data shows promising initial results.

Original languageEnglish
Title of host publicationProceedings of the 14th International Society for Music Information Retrieval Conference, ISMIR 2013
EditorsAlceu de Souza Britto, Fabien Gouyon, Simon Dixon
PublisherInternational Society for Music Information Retrieval
Pages65-70
Number of pages6
ISBN (Electronic)9780615900650
StatePublished - 2013
Externally publishedYes
Event14th International Society for Music Information Retrieval Conference, ISMIR 2013 - Curitiba, Brazil
Duration: 4 Nov 20138 Nov 2013

Publication series

NameProceedings of the 14th International Society for Music Information Retrieval Conference, ISMIR 2013

Conference

Conference14th International Society for Music Information Retrieval Conference, ISMIR 2013
Country/TerritoryBrazil
CityCuritiba
Period4/11/138/11/13

All Science Journal Classification (ASJC) codes

  • Music
  • Information Systems

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