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

• Autor(a) principal: Scalvenzi, Rafael Rubiati [UNESP]
• Data de Publicação: 2019
• Outros Autores: Guido, Rodrigo Capobianco [UNESP], Marranghello, Norian [UNESP]
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Institucional da UNESP
• Texto Completo: http://dx.doi.org/10.1142/S1793351X19500028; http://hdl.handle.net/11449/201209
• topic: pattern recognition; Semantics in digital music; support vector machine; wavelet-packets

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.