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A Supervised Classification Algorithm for Note Onset Detection

EURASIP Journal on Advances in Signal Processing volume 2007, Article number: 043745 (2006) Cite this article

Abstract

This paper presents a novel approach to detecting onsets in music audio files. We use a supervised learning algorithm to classify spectrogram frames extracted from digital audio as being onsets or nononsets. Frames classified as onsets are then treated with a simple peak-picking algorithm based on a moving average. We present two versions of this approach. The first version uses a single neural network classifier. The second version combines the predictions of several networks trained using different hyperparameters. We describe the details of the algorithm and summarize the performance of both variants on several datasets. We also examine our choice of hyperparameters by describing results of cross-validation experiments done on a custom dataset. We conclude that a supervised learning approach to note onset detection performs well and warrants further investigation.

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Authors and Affiliations

  1. Department of Computer Science, University of Montreal, Montreal, QC, H3T 1J4, Canada

    Alexandre Lacoste & Douglas Eck

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  1. Alexandre Lacoste
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  2. Douglas Eck
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Correspondence to Alexandre Lacoste.

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Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License ( https://creativecommons.org/licenses/by/2.0 ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Lacoste, A., Eck, D. A Supervised Classification Algorithm for Note Onset Detection. EURASIP J. Adv. Signal Process. 2007, 043745 (2006). https://doi.org/10.1155/2007/43745

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