Cluster aware normalization for enhancing audio similarity

Mathieu Lagrange*, Luis Gustavo Martins, George Tzanetakis

*Autor correspondente para este trabalho

Resultado de pesquisarevisão de pares

Resumo

An important task in Music Information Retrieval is content-based similarity retrieval in which given a query music track, a set of tracks that are similar in terms of musical content are retrieved. A variety of audio features that attempt to model different aspects of the music have been proposed. In most cases the resulting audio feature vector used to represent each music track is high dimensional. It has been observed that high dimensional music similarity spaces exhibit some anomalies: hubs which are tracks that are similar to many other tracks, and orphans which are tracks that are not similar to most other tracks. These anomalies are an artifact of the high dimensional representation rather than actually based on the musical content. In this work we describe a distance normalization method that is shown to reduce the number of hubs and orphans. It is based on post-processing the similarity matrix that encodes the pair-wise track similarities and utilizes clustering to adapt the distance normalization to the local structure of the feature space.
Idioma originalEnglish
Título da publicação do anfitrião2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012 - Proceedings
Páginas1969-1972
Número de páginas4
DOIs
Estado da publicaçãoPublicado - 2012
Evento2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012 - Kyoto
Duração: 25 mar. 201230 mar. 2012

Série de publicação

NomeICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (impresso)1520-6149

Conferência

Conferência2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012
País/TerritórioJapan
CidadeKyoto
Período25/03/1230/03/12

Impressão digital

Mergulhe nos tópicos de investigação de “Cluster aware normalization for enhancing audio similarity“. Em conjunto formam uma impressão digital única.

Citação