| Texto completo | |
| Autor(es): |
Silva, Diego F.
[1, 2]
;
Yeh, Chin-Chia M.
[3]
;
Zhu, Yan
[3]
;
Batista, Gustavo E. A. P. A.
[1]
;
Keogh, Eamonn
[3]
Número total de Autores: 5
|
| Afiliação do(s) autor(es): | [1] Univ Sao Paulo, Inst Ciencias Matemat & Computacao, BR-13566590 Sao Paulo - Brazil
[2] Univ Fed Sao Carlos, Dept Computacao, BR-13565905 Sao Paulo - Brazil
[3] Univ Calif Riverside, Dept Comp Sci & Engn, Riverside, CA 92521 - USA
Número total de Afiliações: 3
|
| Tipo de documento: | Artigo Científico |
| Fonte: | IEEE TRANSACTIONS ON MULTIMEDIA; v. 21, n. 1, p. 29-38, JAN 2019. |
| Citações Web of Science: | 1 |
| Resumo | |
Most algorithms for music data mining and retrieval analyze the similarity between feature sets extracted from the raw audio. A conventional approach to assess similarities within or between recordings is to create similarity matrices. However, this method requires quadratic space for each comparison and typically requires costly post-processing of the matrix. We have recently proposed SiMPle, a powerful representation based on subsequence similarity join, which is applicable in several music analysis tasks. In this paper, we propose SiMPle-Fast a highly efficient method for exact computation of SiMPle that is up to one order of magnitude faster than SiMPle. Furthermore, we demonstrate the utility of SiMPle-Fast in cover music recognition and thumbnailing tasks and show that our method is significantly faster and more accurate than the state-of-the-art. (AU) | |
| Processo FAPESP: | 16/04986-6 - Armadilhas e sensores inteligentes: uma abordagem inovadora para controle de insetos peste e vetores de doenças |
| Beneficiário: | Gustavo Enrique de Almeida Prado Alves Batista |
| Modalidade de apoio: | Auxílio à Pesquisa - Programa eScience e Data Science - Regular |
| Processo FAPESP: | 13/26151-5 - Análise de séries temporais por similaridade em larga escala |
| Beneficiário: | Diego Furtado Silva |
| Modalidade de apoio: | Bolsas no Brasil - Doutorado |