Visualizing music and audio using self-similarity
MULTIMEDIA '99 Proceedings of the seventh ACM international conference on Multimedia (Part 1)
Template-based estimation of time-varying tempo
EURASIP Journal on Applied Signal Processing
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
An experimental comparison of audio tempo induction algorithms
IEEE Transactions on Audio, Speech, and Language Processing
On Learning, Representing, and Generalizing a Task in a Humanoid Robot
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Audio, Speech, and Language Processing
IEEE Transactions on Audio, Speech, and Language Processing
Proceedings of the 20th ACM international conference on Multimedia
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Most current tempo estimation algorithms suffer from the so-called octave estimation problems (estimating twice, thrice, half or one-third of a reference tempo). However, it is difficult to qualify an error as octave error without a clear definition of what is the reference tempo. For this reason, and given that tempo is mostly a perceptual notion, we study here the estimation of perceptual tempo. We consider the perceptual tempo as defined by the results of the large-scale experiment made at Last-FM in 2011. We assume that the perception of tempo is related to the rate of variation of four musical attributes: the variation of energy, of harmonic changes, of spectral balance and short-term-event-repetitions. We then propose the use of GMM-Regression to find the relationship between the perceptual tempo and the four musical attributes. In an experiment, we show that the estimation of the tempo provided by GMM-Regression over these attributes outperforms the one provided by a state-of-the-art tempo estimation algorithm. For this task GMM-Regression also largely outperforms SVM-Regression. We finally study the estimation of three perceptual tempo classes ("Slow", "In Between", "Fast") using both GMM-Regression and SVM-Classification.