Perceptual tempo estimation using GMM-Regression

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 largescale 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-eventrepetitions. 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.