We present an algorithm that predicts musical genre and artist from an audio waveform. Our
method uses the ensemble learner A DA B OOST to select from a set of audio features that …

We argue that an evaluation of system behavior at the level of the music is required to
usefully address the fundamental problems of music genre recognition (MGR), and indeed …

Much work is focused upon music genre recognition (MGR) from audio recordings, symbolic
data, and other modalities. While reviews have been written of some of this work before, no …

Research in automatic genre classification has been producing increasingly small
performance gains in recent years, with the result that some have suggested that such …

Music is present in everyday life and used for a wide range of objectives. Musical databases
have considerably increased in number and size over the past years, therefore, the …

A library of 160 high-level features is presented along with jSymbolic, a software package
that extracts these features from MIDI files. jSymbolic is intended both as a platform for …

La classification automatique de la musique est un vaste domaine de recherche,
multidisciplinaire par nature, et qui donne lieu à des avancées significatives tant du point de …

In this paper we use content-based features to perform automatic classification of music
pieces into genres. We categorise these features into four groups: features extracted from …

Facing the rapidly growing amount of digital media, the need for an effective data
management is challenging technology. In this context, we approach the problem of …

For music information retrieval tasks, a nearest neighbor classifier using the Kullback-Leibler
divergence between Gaussian mixture models of songs' melfrequency cepstral coefficients …