Deep learning research aims at discovering learning algorithms that discover multiple levels
of distributed representations, with higher levels representing more abstract concepts …

Feature extraction is a crucial part of many MIR tasks. In this work, we present a system that
can automatically extract relevant features from audio for a given task. The feature extraction …

End-to-end learning with convolutional neural networks (CNNs) has become a standard
approach in image classification. However, in audio classification, CNN-based models that …

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 …

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 …

In this work we present a system to automatically learn features from audio in an
unsupervised manner. Our method first learns an overcomplete dictionary which can be …

Recently there has been a great deal of attention paid to the automatic prediction of tags for
music and audio in general. Social tags are user-generated keywords associated with some …

There has been an increasing attention on learning feature representations from the
complex, high-dimensional audio data applied in various music information retrieval (MIR) …

Digital music has become prolific in the web in recent decades. Automated recommendation
systems are essential for users to discover music they love and for artists to reach …

This paper deals with two problems:(1) the selection of a set of music features in order to
achieve high genre classification accuracies;(2) the absence of a representative music …