In this paper we present an approach to music genre classification which converts an audio
signal into spectrograms and extracts texture features from these time-frequency images …

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 …

Every type of musical data (audio, symbolic, lyrics, etc.) has its limitations, and cannot
always capture all relevant properties of a particular musical category. In contrast to more …

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 …

Feature learning and deep learning have drawn great attention in recent years as a way of
transforming input data into more effective representations using learning algorithms. Such …

In this paper we compare two different textural feature sets for automatic music genre
classification. The idea is to convert the audio signal into spectrograms and then extract …

Accurate music tag classification of music clips has been attracting great attention recently,
because it allows one to provide various music excerpts, including unpopular ones, to users …

The interest in the music classification has increased due to its wide applicability and
discoveries obtained from researches. However, efficient methods for systemic organization …

Current research on the task of automatic music genre classification has been focusing on
new classification approaches based on combining information from other sources than the …

In this work, several strategies are developed to reduce the impact of the two limitations of
most current studies in supervised music classification: the classification rules and music …