Onset detection in musical audio signals

Proc. DAFX, 2003

We present a novel method for onset detection in musical signals. It improves over previous energy-based and phase-based approaches by combining both types of information in the complex domain. It generates a detection function that is sharp at the position of onsets and smooth everywhere else. Results on a handlabelled data-set show that high detection rates can be achieved at very low error rates. The approach is more robust than its predecessors both theoretically and practically.

IEEE Journal of Selected Topics in Signal Processing, 2011

2004

This paper addresses the problem of the performance evaluation of algorithms for the automatic detection of note onsets in music signals. Our experiments show that creating a database of reference files with reliable humanannotated onset times is a complex task, since its subjective part cannot be neglected. This work provides a methodology to construct such a database. With the use of a carefully designed software tool, called SOL (Sound Onset Labellizer), we can obtain a set of reference onset times that are cross-validated amongst different expert listeners. We show that the mean error of annotated times across test subjects is very much signal-dependent. This value can be used, when evaluating automatic labelling, as an indication of the relevant tolerance window. The SOL annotation software is to be released freely for research purposes. Our test library, 17 short sequences containing about 750 onsets, comes from copyright-free music or from the public RWC database. The corresponding validated onset labels are also freely distributed, and are intended to form the starting point for the definition of a reliable benchmark.

2010

Abstract:-In this paper, we propose a musical onset detection method, with reference to polyphonic piano music. The solution proposed consists of an onset detection algorithm based on Short-Time Fourier Transform (STFT) and Non-Negative Matrix Factorization (NMF). This method operates on a frame-by-frame basis and exploits a suitable binary time-frequency representation of the audio signal.

2018 Twenty Fourth National Conference on Communications (NCC), 2018

Onset detection refers to the estimation of the timing of events in a music signal. It is an important sub-task in music information retrieval and forms the basis of high-level tasks such as beat tracking and tempo estimation. Typically, the onsets of new events in the audio such as melodic notes and percussive strikes are marked by short-time energy rises and changes in spectral distribution. However, each musical instrument is characterized by its own peculiarities and challenges. In this work, we consider the accurate detection of onsets in piano music. An annotated dataset is presented. The operations in a typical onset detection system are considered and modified based on specific observations on the piano music data. In particular, the use of energy-based weighting of multi-band onset detection functions and the use of a new criterion for adapting the final peak-picking threshold are shown to improve the detection of soft onsets in the vicinity of loud notes. We further present a grouping algorithm which reduces spurious onset detections.

2018 52nd Asilomar Conference on Signals, Systems, and Computers, 2018

Musical onset detection is a key component in any beat tracking system. Existing onset detection methods are based on temporal/spectral analysis, or methods that integrate temporal and spectral information together with statistical estimation and machine learning models. In this paper, we propose a method to localize onset components in music by using the S-transform, and thus, the method is purely based on temporal/spectral data. Unlike the other methods based on temporal/spectral data, which usually rely on the short time Fourier transform (STFT), our method enables effective isolation of crucial frequency subbands due to the frequency dependent resolution of S-transform. Moreover, numerical results show, even with less computationally intensive steps, the proposed method can closely resemble the performance of more resource intensive statistical estimation based approaches.

ABSTRACT Finding the starting time of musical notes in an audio signal, that is, to perform onset detection, is an important task as this information can be used as the basis for high-level musical processing tasks. Many different methods exist to perform onset detection. However their results depend on a Peak Selection step that makes the decision whether an onset is present at some point in time.

Proc. 4th European …, 2003

In this paper, we present a new approach to solving the problem of sound onset detection for note based segmentation of musical audio. The more traditional solu-tion of looking at differences in energy, and the more recently proposed approach of using deviations in expected ...

2015

In this paper a comprehensive online music onset detection algorithm is introduced where – in contrast to many other relevant publications – 14 important algorithm parameters are optimized simultaneously. For solving the optimization problem we derive an extensive tool for iterative model based optimization. In each iteration, a very time consuming evaluation has to be performed on a large music data base. To speed up this procedure, the expected performance of each newly proposed setting is estimated in a pretest on a representative part of the data so that just very promising points are evaluated on all data. We compare different variants of the classical and the fast optimization strategies with respect to the F -values of their best identified parameter settings. The performance of the fast approach appears to be competitive with the classical one while saving more than 80% of music piece evaluations on average. Our best found parameter settings, both for online and offline onse...

SYSTEM, 2010

Onset detection is a key application in music processing. Beat detection algorithms and some music transcribers usually perform onset detection as the starting point of their processing. In music transcription of polyphonic signals, onset detection is very helpful because it aids to place note-event starting times. In this paper, a new technique to implement an onset detection system is proposed. In sinusoidal modelling, the energy burst of non-stationary tones are detected by means of linear prediction in the frequency domain. In frequency, the tone peak and its nearby samples does not match with the window transform when the tone is not stationary at the current frame. This property can bedetected whith linear prediction in the frequency domain. When perceptually significant tones are detected as unstable in a time frame, the system alerts about an onset at this frame. The proposed onset detection system is evaluated over two sound databases obtaining encouraging results.