Author(s): Scalvenzi, Rafael Rubiati [UNESP] ; Guido, Rodrigo Capobianco [UNESP] ; Marranghello, Norian [UNESP]
Date: 2020
Persistent ID: http://hdl.handle.net/11449/201209
Origin: Oasisbr
Subject(s): pattern recognition; Semantics in digital music; support vector machine; wavelet-packets; pattern recognition; pattern recognition; Semantics in digital music; Semantics in digital music; support vector machine; support vector machine; wavelet-packets; wavelet-packets
Made available in DSpace on 2020-12-12T02:26:52Z (GMT). No. of bitstreams: 0 Previous issue date: 2019-09-01
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.
Instituto de Biociências Letras e Ciências Exatas Unesp-Univ Estadual Paulista São Paulo State University, Rua Cristóvão Colombo 2265
Instituto de Biociências Letras e Ciências Exatas Unesp-Univ Estadual Paulista São Paulo State University, Rua Cristóvão Colombo 2265
