A Supervised Classification Algorithm for Note Onset Detection. (5th November 2006)

• Record Type: Journal Article
• Authors: Lacoste Lacoste, Alexandre Alexandre; Eck Eck, Douglas Douglas
• Other Names: Fujinaga Fujinaga Ichiro Ichiro Academic Editor.
• Is Part Of: EURASIP journal on advances in signal processing. Volume 2007(2007)
• Publication Date: 2006-11-05
• Subjects: Signal processing -- Periodicals; Traitement du signal; Signal processing; Periodicals; 621.3822
• DOI: 10.1155/2007/43745
• Languages: English
• ISSNs: 1687-6172

Abstract

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.