Using hyper-genre training to explore genre information for automatic chord estimation

Yizhao Ni*, Matt Mcvicar, Raul Santos-Rodríguez, Tijl De Bie

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

8 Citations (Scopus)

Abstract

Recently a large amount of new chord annotations have been made available. This raises hopes for further development in automatic chord estimation. While more data seems to imply better performance, a major challenge however, is the wide variety of genres covered by these new data. As a result, the genre-independent training scheme as is common today is bound to fail. In this paper we investigate various options for exploring genre information for chord estimation, while also maximally exploiting the full dataset. More specifically, we propose a hyper-genre training scheme in which each genre cluster has its own parameters, tied together by hyper parameters as a Bayesian prior. The results are promising, showing significant improvements over other prevailing training schemes.

Original languageEnglish
Title of host publicationProceedings of the 13th International Society for Music Information Retrieval Conference, ISMIR 2012
Pages109-114
Number of pages6
Publication statusPublished - 2012
Event13th International Society for Music Information Retrieval Conference, ISMIR 2012 - Porto, Portugal
Duration: 8 Oct 201212 Oct 2012

Publication series

NameProceedings of the 13th International Society for Music Information Retrieval Conference, ISMIR 2012

Conference

Conference13th International Society for Music Information Retrieval Conference, ISMIR 2012
Country/TerritoryPortugal
CityPorto
Period8/10/1212/10/12

Bibliographical note

Copyright:
Copyright 2013 Elsevier B.V., All rights reserved.

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