Wavelet-packets Associated with Support Vector Machine Are Effective for Monophone Sorting in Music Signals

Autor(es)

Scalvenzi, Rafael Rubiati ^[1] ; Guido, Rodrigo Capobianco ^[1] ; Marranghello, Norian ^[1]

Afiliação do(s) autor(es)

^[1] Sao Paulo State Univ, Univ Estadual Paulista, UNESP, Inst Biociencias Letras & Ciencias Exatas, Rua Cristovao Colombo 2265, BR-15054000 Sao Jose Do Rio Preto, SP - Brazil

• Tipo de documento: Artigo Científico
• Fonte: INTERNATIONAL JOURNAL OF SEMANTIC COMPUTING; v. 13, n. 3, p. 415-425, SEP 2019.
• Citações Web of Science: 0

Resumo

An abstract interpretation is usually required to analyze acoustic compositions. Nevertheless, there is much signal processing-related research focusing on music processing and similar topics. In that context, the semantic information contained in the melody involving major and minor chords, sharps and flats associated with semibreve, minim, crotchet, quaver, semiquaver and demisemiquaver notes can help in the study of musical sounds. Thus, multiresolution analysis based on discrete wavelet-packet transform (DWPT) associated with a support vector machine (SVM) is used in this paper to inspect and classify those signals, correlating them with a respective acoustic pattern. Results over hundreds of inputs provided almost full accuracy, reassuring the efficacy of the proposed approach for both off-line and real-time usage. (AU)

• Processo FAPESP: 19/04475-0 - Análise Paraconsistente de Características dos Sinais de Fala: combatendo os ataques de voice spoofing
• Beneficiário: Rodrigo Capobianco Guido
• Modalidade de apoio: Auxílio à Pesquisa - Regular