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 …

The proliferation of MP3 players and the exploding amount of digital music content call for
novel ways of music organization and retrieval to meet the ever-increasing demand for easy …

Openly available datasets are a key factor in the advancement of data-driven research
approaches, including many of the ones used in sound and music computing. In the last few …

We introduce MedleyDB: a dataset of annotated, royaltyfree multitrack recordings. The
dataset was primarily developed to support research on melody extraction, addressing …

Crowd sourcing is evolving as a distributed problem-solving and business production model
in recent years. In crowd sourcing paradigm, tasks are distributed to networked people to …

Recent developments have demonstrated the capacity of restricted Boltzmann machines
(RBM) to be powerful generative models, able to extract useful features from input data or …

Along with the rapid expansion of digital music formats, managing and searching for songs
has become significant. Though music information retrieval (MIR) techniques have been …

Feature extraction is a crucial part of many MIR tasks. In this work, we present a system that
can automatically extract relevant features from audio for a given task. The feature extraction …

Scientists and researchers have tried to establish a bond between the emotions conveyed
and the subsequent mood perceived in a person. Emotions play a major role in terms of our …

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 …