Polyphonic music classification on symbolic data using dissimilarity functions

Yoko Anan, kohei hatano, Hideo Bannai, Masayuki Takeda, Ken Satoh

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

8 Citations (Scopus)

Abstract

This paper addresses the polyphonic music classification problem on symbolic data. A new method is proposed which converts music pieces into binary chroma vector sequences and then classifies them by applying the dissimilarity-based classification method TWIST proposed in our previous work. One advantage of using TWIST is that it works with any dissimilarity measure. Computational experiments show that the proposed method drastically outperforms SVM and k-NN, the state-of-the-art classification methods.

Original languageEnglish
Title of host publicationProceedings of the 13th International Society for Music Information Retrieval Conference, ISMIR 2012
Pages229-234
Number of pages6
Publication statusPublished - 2012
Event13th International Society for Music Information Retrieval Conference, ISMIR 2012 - Porto, Portugal
Duration: Oct 8 2012Oct 12 2012

Other

Other13th International Society for Music Information Retrieval Conference, ISMIR 2012
CountryPortugal
CityPorto
Period10/8/1210/12/12

All Science Journal Classification (ASJC) codes

  • Music
  • Information Systems

Fingerprint Dive into the research topics of 'Polyphonic music classification on symbolic data using dissimilarity functions'. Together they form a unique fingerprint.

Cite this