A decade has passed since the first review of research on a 'flagship application'of music
information retrieval (MIR): the problem of music genre recognition (MGR). During this time …

With the rapid development of high-tech multimedia technologies, many musical resource
assets are available online and it has always triggered an interest in the classification of …

Music genre recognition is a very interesting area of research in the broad scope of music
information retrieval and audio signal processing. In this work we propose a novel approach …

The GTZAN dataset appears in at least 100 published works, and is the most-used public
dataset for evaluation in machine listening research for music genre recognition (MGR). Our …

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 …

Solving real-world classification and recognition problems requires a principled way of
modeling the physical phenomena generating the observed data and the uncertainty in it …

A significant amount of work in automatic music genre recognition has used a dataset whose
composition and integrity has never been formally analyzed. For the first time, we provide an …

Abstract Deep Neural Network (DNN) models have lately received considerable attention for
that the network structure can extract deep features to improve classification accuracy and …

Motivated by the rich, psycho-physiologically grounded properties of auditory cortical
representations and the power of sparse representation-based classifiers, we propose a …