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Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article A Supervised Classification Algorithm for Note Onset Detection | Hindawi Publishing Corporation EURASIP Journal on Advances in Signal Processing Volume 2007 Article ID 43745 13 pages doi 10.1155 2007 43745 Research Article A Supervised Classification Algorithm for Note Onset Detection Alexandre Lacosteand Douglas Eck Department of Computer Science University of Montreal Montreal QC Canada H3T1J4 Received 5 December 2005 Revised 9 August 2006 Accepted 26 August 2006 Recommended by Ichiro Fujinaga 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. Copyright 2007 Hindawi Publishing Corporation. All rights reserved. 1. INTRODUCTION This paper is concerned with finding the onset times of notes in music audio. Though conceptually simple this task is deceivingly difficult to perform automatically with a computer. Consider for example the naive approach of finding amplitude peaks in the raw waveform. This strategy fails except for trivially easy cases such as monophonic percussive instruments. At the same time onset detection is implicated in a number of important music information retrieval MIR tasks and thus warrants research. Onset detection is useful in the analysis of temporal structure in music such as tempo .
