A tutorial on onset detection in music signals
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Citations
On the Nature and Types of Anomalies: A Review of Deviations in Data
Physically Informed Subtraction of a String's Resonances from Monophonic, Discretely Attacked Tones : a Phase Vocoder Approach
An Investigative High-Level Design of an Electric Bass Tutoring System Integrating Game Elements
Blind Single Channel Sound Source Separation
References
Detection of abrupt changes: theory and application
Auditory Scene Analysis: The Perceptual Organization of Sound
Introduction to the Psychology of Hearing
Speech analysis/Synthesis based on a sinusoidal representation
A model for the prediction of thresholds, loudness, and partial loudness
Related Papers (5)
Frequently Asked Questions (9)
Q2. What are the contributions mentioned in the paper "A tutorial on onset detection in music signals" ?
The goal of this paper is to review, categorize, and compare some of the most commonly used techniques for onset detection, and to present possible enhancements. The authors discuss methods based on the use of explicitly predefined signal features: the signal ’ s amplitude envelope, spectral magnitudes and phases, time-frequency representations ; and methods based on probabilistic signal models: model-based change point detection, surprise signals, etc. Using a choice of test cases, the authors provide some guidelines for choosing the appropriate method for a given application.
Q3. What is the way to detect onsets?
For nontrivial sounds, onset detection schemes benefit from using richer representations of the signal (e.g., a time-frequency representation).
Q4. What is the expectation of the log-likelihood ratio?
Under model , the expectation is(15)If the authors assume that the signal initially follows model , and switches to model at some unknown time, then the short-time average of the log-likelihood ratio will change sign.
Q5. What is the scheme for detecting transients?
The scheme takes advantage of the correlations across scales of the coefficients: large wavelet coefficients, related to transients in the signal, are not evenly spread within the dyadic plane but rather form “structures”.
Q6. What is the procedure used in the majority of onset detection algorithms?
Fig. 2 illustrates the procedure employed in the majority of onset detection algorithms: from the original audio signal, which can be pre-processed to improve the performance of subsequent stages, a detection function is derived at a lower sampling rate, to which a peak-picking algorithm is applied to locate the onsets.
Q7. What is the alternative to the analysis of the temporal envelope of the signal and of Fourier?
An alternative to the analysis of the temporal envelope of the signal and of Fourier spectral coefficients, is the use of time-scale or timefrequency representations (TFR).
Q8. What is the general approach to detecting a frequency?
A more general approach based on changes in the spectrum is to formulate the detection function as a “distance” between successive short-term Fourier spectra, treating them as points in an -dimensional space.
Q9. What was the threshold for the peak-picking parameters?
All peak-picking parameters (e.g., filter’s cutoff frequency, ) were held constant, except for the threshold which was varied to trace out the performance curve.