Music information retrieval (MIR) is an emerging research area that receives growing
attention from both the research community and music industry. It addresses the problem of …

A huge increase in the number of digital music tracks has created the necessity to develop
an automated tool to extract the useful information from these tracks. As this information has …

Content-based retrieval has emerged in the face of content explosion as a promising
approach to information access. In this paper, we focus on the challenging issue of …

In this paper, we will propose an automatic music genre classification approach based on
long-term modulation spectral analysis of spectral (OSC and MPEG-7 NASE) as well as …

Determining the emotion of a song that best characterizes the affective content of the song is
a challenging issue due to the difficulty of collecting reliable ground truth data and the …

Nowadays, it appears essential to design automatic indexing tools which provide
meaningful and efficient means to describe the musical audio content. There is in fact a …

Feature extraction and dimensionality reduction are important tasks in many fields of science
dealing with signal processing and analysis. The relevance of these techniques is …

Music recommendation has been one of the most used information retrieval services on
internet. Finding suitable music for users' demands from tens of millions of music relies on …

During recent years, the field of emotional content analysis of speech signals has been
gaining a lot of attention and several frameworks have been constructed by different …

Deep Learning, a branch of Machine Learning is a rapidly expanding field in the Industry 4.0
revolution. The number of applications of Deep Learning are enormous-finding multiple …