We provide a survey of the field of Music Information Retrieval (MIR), in particular paying
attention to latest developments, such as semantic auto-tagging and user-centric retrieval …

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 …

This chapter gives an introduction to music recommender systems research. We highlight
the distinctive characteristics of music, as compared to other kinds of media. We then …

Originating from Facebook, LinkedIn, Twitter, Instagram, YouTube, and many other
networking sites, the social media shared by users and the associated metadata are …

This chapter gives an introduction to music recommender systems, considering the unique
characteristics of the music domain. We take a user-centric perspective, by organizing 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 …

This paper presents findings about mood representations. We aim to analyze how do people
tag music by mood, to create representations based on this data and to study the agreement …

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 …

Machine learning has been used to accurately classify musical genre using features derived
from audio signals. Musical genre, as well as lower-level audio features of music, have also …

Much of the existing research on user aspects in the music information retrieval field tends to
focus on general user needs or behavior related to music information seeking, music …