A Tutorial on Deep Learning for Music Information Retrieval

TL;DR

This paper provides a comprehensive tutorial on applying deep learning techniques to Music Information Retrieval, aiming to assist beginners and promote original research in the field by reviewing methods, structures, and advanced topics.

Contribution

It offers an accessible overview of deep learning in MIR, including network structures, guidelines for new tasks, and insights to encourage innovative research.

Findings

Reviewed successful deep learning architectures in MIR

Provided guidelines for selecting network components

Discussed advanced topics to stimulate new research

Abstract

Following their success in Computer Vision and other areas, deep learning techniques have recently become widely adopted in Music Information Retrieval (MIR) research. However, the majority of works aim to adopt and assess methods that have been shown to be effective in other domains, while there is still a great need for more original research focusing on music primarily and utilising musical knowledge and insight. The goal of this paper is to boost the interest of beginners by providing a comprehensive tutorial and reducing the barriers to entry into deep learning for MIR. We lay out the basic principles and review prominent works in this hard to navigate the field. We then outline the network structures that have been successful in MIR problems and facilitate the selection of building blocks for the problems at hand. Finally, guidelines for new tasks and some advanced topics in deep learning are discussed to stimulate new research in this fascinating field.