Music Onset Detection

Music Onset Detection

Ruohua Zhou (Queen Mary University of London, UK) and Josh D. Reiss (Queen Mary University of London, UK)
Copyright: © 2011 |Pages: 20
DOI: 10.4018/978-1-61520-919-4.ch012
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Abstract

Music onset detection plays an essential role in music signal processing and has a wide range of applications. This chapter provides a step by step introduction to the design of music onset detection algorithms. The general scheme and commonly-used time-frequency analysis for onset detection are introduced. Many methods are reviewed, and some typical energy-based, phase-based, pitch-based and supervised learning methods are described in detail. The commonly used performance measures, onset annotation software, public database and evaluation methods are introduced. The performance difference between energy-based and pitch-based method is discussed. The future research directions for music onset detection are also described.
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Algorithms

General Scheme

Many different onset detection systems have been described in the literature. As shown in Figure 1, they typically consist of three stages; time-frequency processing, detection function generation, and peak-picking (Bello et al., 2005). At first, a music signal is transformed into different frequency bands by using a filter-bank or a spectrogram. For example, the Short Time Fourier Transform (STFT) and the Resonator Time Frequency Image (RTFI) are two useful time-frequency analysis tools for onset detection. Then, the output of the first stage is further processed to generate a detection function at a lower sampling rate. Finally, a peak-picking operation is used to find onset times within the detection function, which is often derived by inspecting the changes in energy, phase, or pitch.

Figure 1.

Three stages of music onset detection: time-frequency processing of the audio signal, producing an onset detection function, and using peak-picking to identify onsets

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