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 great number of deep learning based models have been recently proposed for automatic
music composition. Among these models, the Transformer stands out as a prominent …

Preference elicitation is a challenging fundamental problem when designing recommender
systems. In the present work we propose a content-based technique to automatically …

Measuring music similarity is essential for multimedia retrieval. For music items, this task can
be regarded as obtaining a suitable distance measurement between songs defined on a …

It is not hyperbole to note that a revolution has occurred in the way that we as a society
distribute data and information. This revolution has come about through the confluence of …

Similarity is a key concept for estimating associations among a set of objects. Music
similarity is usually exploited to retrieve relevant items from a dataset containing audio …

Computational modelling of music similarity is an increasingly important part of
personalisation and optimisation in music information retrieval and research in music …

The amount of digital music has grown unprecedentedly during the last years and requires
the development of effective methods for search and retrieval. In particular, content-based …

The music we like (ie our musical preferences) encodes and communicates key information
about ourselves. Depicting such preferences in a condensed and easily understandable …