Music Classification: Beyond Supervised Learning, Towards Real-world Applications
Abstract
Music classification is a music information retrieval (MIR) task to classify music items to labels such as genre, mood, and instruments. It is also closely related to other concepts such as music similarity and musical preference. In this tutorial, we put our focus on two directions - the recent training schemes beyond supervised learning and the successful application of music classification models. The target audience for this web book is researchers and practitioners who are interested in state-of-the-art music classification research and building real-world applications. We assume the audience is familiar with the basic machine learning concepts. In this book, we present three lectures as follows: 1. Music classification overview: Task definition, applications, existing approaches, datasets, 2. Beyond supervised learning: Semi- and self-supervised learning for music classification, 3. Towards real-world applications: Less-discussed, yet important research issues in practice.
Cite
@article{arxiv.2111.11636,
title = {Music Classification: Beyond Supervised Learning, Towards Real-world Applications},
author = {Minz Won and Janne Spijkervet and Keunwoo Choi},
journal= {arXiv preprint arXiv:2111.11636},
year = {2021}
}
Comments
This is a web book written for a tutorial session of the 22nd International Society for Music Information Retrieval Conference, Nov 8-12, 2021. Please visit https://music-classification.github.io/tutorial/ for the original, web book format