# Music Classification: Beyond Supervised Learning, Towards Real-world Applications

arXiv: https://arxiv.org/abs/2111.11636 · HTML: https://mlsanity.com/paper/2111.11636/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Nov 2021.

## Built on by (most cited first, 7 of 7)

- [LC-Protonets: Multi-Label Few-Shot Learning for World Music Audio Tagging](https://mlsanity.com/paper/2409.11264/) (2024)
- [Jointist: Joint Learning for Multi-instrument Transcription and Its Applications](https://mlsanity.com/paper/2206.10805/) (2022)
- [I can listen but cannot read: An evaluation of two-tower multimodal systems for instrument recognition](https://mlsanity.com/paper/2407.18058/) (2024)
- [Jointist: Simultaneous Improvement of Multi-instrument Transcription and Music Source Separation via Joint Training](https://mlsanity.com/paper/2302.00286/) (2023)
- [MDSC: Towards Evaluating the Style Consistency Between Music and Dance](https://mlsanity.com/paper/2309.01340/) (2023)
- [Prevailing Research Areas for Music AI in the Era of Foundation Models](https://mlsanity.com/paper/2409.09378/) (2024)
- [CloserMusicDB: A Modern Multipurpose Dataset of High Quality Music](https://mlsanity.com/paper/2410.19540/) (2024)

## Abstract (arXiv)

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.