Automatic classification of instruments from supervised methods of machine learning

  • Rômulo Vieira Universidade Federal de São João del-Rei
  • João Teixeira Araújo Universidade Federal de São João del-Rei
  • Edimilson Batista Universidade Federal de São João del-Rei
  • Flávio Luiz Schiavoni Universidade Federal de São João del-Rei


Sorting instruments is not an easy task for humans or computers, especially when it comes to elements with the same acoustic properties, such as wind, percussion, or strings. Nevertheless, the use of audio descriptors and artificial intelligence techniques can make this duty more accessible. In this paper, three supervised methods, Naive Bayes, decision tree and Support Vector Classifier (SVC) are used to categorize acoustic guitar and bass sounds in a database, using as a parameter the information extracted from audio descriptors. The research resulted in a performance comparison of these three algorithms, considering their hit rates and processing time when classifying samples in different parts of the dataset. After all, some relevant considerations about the feasibility of automatically classifying instruments are presented.

Palavras-chave: Artificial Intelligence, A-Life and Evolutionary Music Systems, Music Analysis and Synthesis, Music Information Retrieval


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VIEIRA, Rômulo; ARAÚJO, João Teixeira; BATISTA, Edimilson; SCHIAVONI, Flávio Luiz. Automatic classification of instruments from supervised methods of machine learning. In: SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO MUSICAL (SBCM), 18. , 2021, Recife. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2021 . p. 1-7. DOI: