SPARSE MUSIC DECOMPOSITION ONTO A MIDI DICTIONARY DRIVEN BY STATISTICAL MUSIC KNOWLEDGE

Boyang Gao 1 Emmanuel Dellandréa 1 Liming Chen 1
1 imagine - Extraction de Caractéristiques et Identification
LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information
Abstract : The general goal of music signal decomposition is to represent the music structure into a note level to provide val- uable semantic features for further music analysis tasks. In this paper, we propose a new method to sparsely decompose the music signal onto a MIDI dictionary made of musical notes. Statistical music knowledge is further integrated into the whole sparse decomposition process. The proposed method is divided into a frame level sparse decomposition stage and a whole music level optimal note path searching. In the first stage note co-occurrence probabilities are embedded to generate a sparse multiple candidate graph while in the second stage note transition probabilities are incorporated into the optimal path searching. Experiments on real-world polyphonic music show that embedding music knowledge within the sparse decomposition achieves notable improvement in terms of note recognition precision and recall.
Type de document :
Communication dans un congrès
International Society for Music Information Retrieval Conference (ISMIR), Nov 2013, Curitiba, Brazil. pp.445-450, 2013
Liste complète des métadonnées
https://hal.archives-ouvertes.fr/hal-01339270
Contributeur : Équipe Gestionnaire Des Publications Si Liris <>
Soumis le : mercredi 29 juin 2016 - 15:51:06
Dernière modification le : vendredi 11 janvier 2019 - 16:50:26

Identifiants

  • HAL Id : hal-01339270, version 1

Collections

EC-LYON | LIRIS | INSA-LYON | UNIV-LYON1 | LABEXIMU

Citation

Boyang Gao, Emmanuel Dellandréa, Liming Chen. SPARSE MUSIC DECOMPOSITION ONTO A MIDI DICTIONARY DRIVEN BY STATISTICAL MUSIC KNOWLEDGE. International Society for Music Information Retrieval Conference (ISMIR), Nov 2013, Curitiba, Brazil. pp.445-450, 2013. 〈hal-01339270〉

Exporter

BibTeX TEI DC DCterms EndNote

Partager

Métriques

Consultations de la notice

128

Contact

support.ccsd.cnrs.fr
hal.support@ccsd.cnrs.fr
Logo CCSD