| Full text | |
| Author(s): |
Silva, Diego F.
[1, 2]
;
Yeh, Chin-Chia M.
[3]
;
Zhu, Yan
[3]
;
Batista, Gustavo E. A. P. A.
[1]
;
Keogh, Eamonn
[3]
Total Authors: 5
|
| Affiliation: | [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
Total Affiliations: 3
|
| Document type: | Journal article |
| Source: | IEEE TRANSACTIONS ON MULTIMEDIA; v. 21, n. 1, p. 29-38, JAN 2019. |
| Web of Science Citations: | 1 |
| Abstract | |
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) | |
| FAPESP's process: | 16/04986-6 - Intelligent traps and sensors: an innovative approach to control insect pests and disease vectors |
| Grantee: | Gustavo Enrique de Almeida Prado Alves Batista |
| Support Opportunities: | Research Grants - eScience and Data Science Program - Regular Program Grants |
| FAPESP's process: | 13/26151-5 - Time series analysis by similarity in large scale |
| Grantee: | Diego Furtado Silva |
| Support Opportunities: | Scholarships in Brazil - Doctorate |