Fast Similarity Matrix Profile for Music Analysis and Exploration

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]

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

• 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
• Linha de fomento: 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
• Linha de fomento: Bolsas no Brasil - Doutorado