Automatic onset detection using convolutional neural networks

  • Willy Cornelissen Federal University of Minas Gerais
  • Maurício Loureiro Federal University of Minas Gerais


A very significant task for music research is to estimate instants when meaningful events begin (onset) and when they end (offset). Onset detection is widely applied in many fields: electrocardiograms, seismographic data, stock market results and many Music Information Research(MIR) tasks, such as Automatic Music Transcription, Rhythm Detection, Speech Recognition, etc. Automatic Onset Detection(AOD) received, recently, a huge contribution coming from Artificial Intelligence (AI) methods, mainly Machine Learning and Deep Learning. In this work, the use of Convolutional Neural Networks (CNN) is explored by adapting its original architecture in order to apply the approach to automatic onset detection on audio musical signals. We used a CNN network for onset detection on a very general dataset, well acknowledged by the MIR community, and examined the accuracy of the method by comparison to ground truth data published by the dataset. The results are promising and outperform another methods of musical onset detection.

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


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CORNELISSEN, Willy; LOUREIRO, Maurício. Automatic onset detection using convolutional neural networks. In: SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO MUSICAL (SBCM), 17. , 2019, São João del-Rei. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2019 . p. 199-200. DOI: