A Fuzzy Approach to Drum Cymbals Classification

Authors

Keywords:

Cymbals, Bronze Alloys, Music Information Retrieval, Fuzzy

Abstract

The many factors that influence the sound of a cymbal, combined with the external aspects that modify its sound perception, make the study of the acoustics of these instruments more challenging. Within the context of machine learning, most researches involving cymbal classification from their sounds aim to identify those instruments according to their types. However, there is a lack of studies investigating the acoustic elements of cymbals using machine learning techniques as tools. Hence, this paper proposes to classify cymbals according to their constitutive materials, since the metallic alloy assumes a significant portion is responsible for their acoustics. In addition, there is an interest in evaluating a fuzzy logic approach as a classifier applied to three sets of attributes, formed from temporal features and Mel Frequency Cepstral Coefficients extracted from audio signals, comparing triangular and Gaussian membership functions. For this, 276 audios, referring to 4 drum cymbals, were collected from a standardized procedure for capturing the sounds that considered variations in microphones and environments. As a result, the implemented model achieved 94.72% of average accuracy with a standard deviation of 2.51%, considering the Gaussian membership function and Mel Frequency Cepstral Coefficients as the audio descriptor.

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Author Biographies

Tales Boratto, Federal University of Juiz de Fora

Mestrando em Modelagem Computacional pela Universidade Federal de Juiz de Fora. Possui graduação em Ciências Exatas (2019) e em Engenharia Mecânica (2020) pela Universidade Federal de Juiz de Fora. Suas pesquisas envolvem as áreas de Ciência dos Materiais, com ênfase em Ligas de Bronze, Processos de Fabricação, Recuperação de Informação Musical (MIR) e Aprendizado de Máquina.

Alexandre Cury, Federal University of Juiz de Fora

Graduado em Engenharia Civil pela Universidade Federal de Juiz de Fora (2006), mestre em Modelagem Computacional pela Universidade Federal de Juiz de Fora (2008) e doutor em Engenharia Civil pela Universidade Paris-Est (École Nationale des Ponts et Chaussées) em 2010. Atua, principalmente, nos seguintes temas: monitoramento de integridade estrutural, análise de vibrações, detecção de danos, identificação modal, análise estatística avançada e confiabilidade estrutural. É professor associado no Departamento de Mecânica Aplicada e Computacional. Foi coordenador da APCN e do Programa de Pós-Graduação em Engenharia Civil da Universidade Federal de Juiz de Fora (2016-2019). Entre 2014 e 2016, foi membro da CA-TEC (Câmara de Assessoramento de Arquitetura e Engenharias) da FAPEMIG (Fundação de Amparo a Pesquisa do Estado de Minas Gerais). É Pesquisador de Produtividade do CNPq desde 2013. Membro do corpo editorial da revista Frontiers: Build Engineering e Structural Sensing. Recebeu menção honrosa no Prêmio CAPES de Tese 2017.

Leonardo Goliatt, Federal University of Juiz de Fora

Possui graduação em Engenharia Civil pela Universidade Federal de Juiz de Fora (2003) e Doutorado em Modelagem Computacional pelo Laboratório Nacional de Computação Científica (2009). Atuou como professor do Departamento de Ciências Matemáticas e Naturais da Universidade Federal do Espírito Santo (2010). Professor associado do Departamento de Mecânica Aplicada e Computacional e membro permanente do Programa de Pós-Graduação em Modelagem Computacional da Universidade Federal de Juiz de Fora. Atuou como chefe do Departamento de Mecânica Aplicada e Computacional (2014-2016). Membro da Comissão Própria de Avaliação da UFJF (2017-2019) e coordenador do Programa de Pós-Graduação em Modelagem Computacional da UFJF (2018-2021).
Tem experiência na área de Computação Evolucionária, Inteligência Computacional e Ciência de Dados, atuando principalmente nos seguintes temas: aprendizado de máquina, meta heurísticas, metamodelos, otimização estrutural e simulação.

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Published

2022-06-27

How to Cite

Boratto, T., Cury, A., & Goliatt, L. (2022). A Fuzzy Approach to Drum Cymbals Classification. IEEE Latin America Transactions, 20(9), 2172–2180. Retrieved from https://latamt.ieeer9.org/index.php/transactions/article/view/6487

Issue

Vol. 20 No. 9 (2022): Ordinary Issue

Section

Computing