Real-time detection of musical onsets with linear prediction and sinusoidal modeling

John Glover,Joseph Timoney,Victor Lazzarini

doi:10.1186/1687-6180-2011-68

John Glover, Joseph Timoney + Show 1 more

Open Access

https://doi.org/10.1186/1687-6180-2011-68

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Abstract

Real-time musical note onset detection plays a vital role in many audio analysis processes, such as score following, beat detection and various sound synthesis by analysis methods. This article provides a review of some of the most commonly used techniques for real-time onset detection. We suggest ways to improve these techniques by incorporating linear prediction as well as presenting a novel algorithm for real-time onset detection using sinusoidal modelling. We provide comprehensive results for both the detection accuracy and the computational performance of all of the described techniques, evaluated using Modal, our new open source library for musical onset detection, which comes with a free database of samples with hand-labelled note onsets.

Highlights

1 Introduction Many real-time musical signal-processing applications depend on the temporal segmentation of the audio signal into discrete note events
In sound synthesis by analysis, the choice of processing algorithm will often depend on the characteristics of the sound source
4 Real-time onset detection using sinusoidal modelling In Section 3, we describe a way to improve the detection accuracy of several onset-detection function (ODF) from the literature using linear prediction (LP) to enhance their estimates of the frame-by-frame evolution of an audio signal

Summary

Introduction

Many real-time musical signal-processing applications depend on the temporal segmentation of the audio signal into discrete note events Systems such as score followers [1] may use detected note events to interact directly with a live performer. For real-time applications of tools such as the Phase Vocoder, it may not be possible to depend on any prior knowledge of the signal to select the processing algorithm, and so we must be able to identify transient regions on-the-fly to reduce synthesis artefacts. It is within this context that onset detection will be studied in this article

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