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 …

Mood is an emerging metadata type and access point in music digital libraries (MDL) and
online music repositories. In this study, we present a comprehensive investigation of the …

Towards improving the performance in various music information processing tasks, recent
studies exploit different modalities able to capture diverse aspects of music. Such modalities …

We present a novel approach for analysing and classifying lyrics, experimenting both with
ngram models and more sophisticated features that model different dimensions of a song …

Music genre classification, especially using lyrics alone, remains a challenging topic in
Music Information Retrieval. In this study we apply recurrent neural network models to …

Music is ubiquitous in our everyday lives, and lyrics play an integral role when we listen to
music. The complex relationships between lyrical content, its temporal evolution over the last …

Music Genre Classification is a significant and practical field of Music Information Retrieval.
Deep learning is increasingly being applied to Music Genre Classification for two main …

Algorithms that can understand and interpret characteristics of music, and organise them for
and recommend them to their users can be of great assistance in handling the ever growing …

Purpose Genre classification of songs based on lyrics is a challenging task even for
humans, however, state-of-the-art natural language processing has recently offered …

This paper describes experimental research investigating the genre classification utility of
combining features extracted from lyrical, audio, symbolic and cultural sources of musical …