# Tuning Music Education: AI-Powered Personalization in Learning Music

arXiv: https://arxiv.org/abs/2412.13514 · HTML: https://mlsanity.com/paper/2412.13514/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 33 references that are in the map.
- First posted on arXiv 18 Dec 2024.

## Builds on (most cited first, 9 of 9)

- [Jukebox: A Generative Model for Music](https://mlsanity.com/paper/2005.00341/) (2020)
- [Simple and Controllable Music Generation](https://mlsanity.com/paper/2306.05284/) (2023)
- [A Hierarchical Latent Vector Model for Learning Long-Term Structure in Music](https://mlsanity.com/paper/1803.05428/) (2018)
- [MT3: Multi-Task Multitrack Music Transcription](https://mlsanity.com/paper/2111.03017/) (2021)
- [High-resolution Piano Transcription with Pedals by Regressing Onset and Offset Times](https://mlsanity.com/paper/2010.01815/) (2020)
- [BeatNet: CRNN and Particle Filtering for Online Joint Beat Downbeat and Meter Tracking](https://mlsanity.com/paper/2108.03576/) (2021)
- [Pop2Piano : Pop Audio-based Piano Cover Generation](https://mlsanity.com/paper/2211.00895/) (2022)
- [PiCoGen: Generate Piano Covers with a Two-stage Approach](https://mlsanity.com/paper/2407.20883/) (2024)
- [PiCoGen2: Piano cover generation with transfer learning approach and weakly aligned data](https://mlsanity.com/paper/2408.01551/) (2024)

## Abstract (arXiv)

Recent AI-driven step-function advances in several longstanding problems in music technology are opening up new avenues to create the next generation of music education tools. Creating personalized, engaging, and effective learning experiences are continuously evolving challenges in music education. Here we present two case studies using such advances in music technology to address these challenges. In our first case study we showcase an application that uses Automatic Chord Recognition to generate personalized exercises from audio tracks, connecting traditional ear training with real-world musical contexts. In the second case study we prototype adaptive piano method books that use Automatic Music Transcription to generate exercises at different skill levels while retaining a close connection to musical interests. These applications demonstrate how recent AI developments can democratize access to high-quality music education and promote rich interaction with music in the age of generative AI. We hope this work inspires other efforts in the community, aimed at removing barriers to access to high-quality music education and fostering human participation in musical expression.