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 stateof-the-art tempo estimation algorithm. For this task GMMRegression 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.
Novel Class Discovery (NCD) is a growing field where we are given during training a labeled set of known classes and an unlabeled set of different classes that must be discovered. In recent years, many methods have been proposed to address this problem, and the field has begun to mature. In this paper, we provide a comprehensive survey of the state-of-the-art NCD methods. We start by formally defining the NCD problem and introducing important notions. We then give an overview of the different families of approaches, organized by the way they transfer knowledge from the labeled set to the unlabeled set. We find that they either learn in two stages, by first extracting knowledge from the labeled data only and then applying it to the unlabeled data, or in one stage by conjointly learning on both sets. For each family, we describe their general principle and detail a few representative methods. Then, we briefly introduce some new related tasks inspired by the increasing number of NCD works. We also present some common tools and techniques used in NCD, such as pseudo labeling, self-supervised learning and contrastive learning. Finally, to help readers unfamiliar with the NCD problem differentiate it from other closely related domains, we summarize some of the closest areas of research and discuss their main differences.
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