This paper surveys the state of the art in automatic emotion recognition in music. Music is
oftentimes referred to as a “language of emotion”[1], and it is natural for us to categorize …

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 …

This paper investigates the use of automatically extracted visual features of videos in the
context of recommender systems and brings some novel contributions in the domain of …

Increasing amount of online music content has opened new opportunities for implementing
new effective information access services–commonly known as music recommender …

Collaborative tagging has become an increasingly popular means for sharing and
organizing Web resources, leading to a huge amount of user generated metadata. These …

In this survey article, we give an overview of methods for music similarity estimation and
music recommendation based on music context data. Unlike approaches that rely on music …

Personalization and context-awareness are highly important topics in research on Intelligent
Information Systems. In the fields of Music Information Retrieval (MIR) and Music …

ABSTRACT A method is proposed for detecting the emotions of Chinese song lyrics based
on an affective lexicon. The lexicon is composed of words translated from ANEW and words …

In this paper we describe a novel approach to applying text-based information retrieval
techniques to music collections. We represent tracks with a joint vocabulary consisting of …

We compare five approaches to collecting tags for music: conducting a survey, harvesting
social tags, deploying annotation games, mining web documents, and autotagging audio …