# PiCoGen: Generate Piano Covers with a Two-stage Approach

arXiv: https://arxiv.org/abs/2407.20883 · HTML: https://mlsanity.com/paper/2407.20883/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Jul 2024.

## Built on by (most cited first, 6 of 6)

- [Time-Shifted Token Scheduling for Symbolic Music Generation](https://mlsanity.com/paper/2509.23749/) (2025)
- [Segment-Factorized Full-Song Generation on Symbolic Piano Music](https://mlsanity.com/paper/2510.05881/) (2025)
- [AMT-APC: Automatic Piano Cover by Fine-Tuning an Automatic Music Transcription Model](https://mlsanity.com/paper/2409.14086/) (2024)
- [Etude: Piano Cover Generation with a Three-Stage Approach -- Extract, strucTUralize, and DEcode](https://mlsanity.com/paper/2509.16522/) (2025)
- [Tuning Music Education: AI-Powered Personalization in Learning Music](https://mlsanity.com/paper/2412.13514/) (2024)
- [Structure-Aware Piano Accompaniment via Style Planning and Dataset-Aligned Pattern Retrieval](https://mlsanity.com/paper/2602.15074/) (2026)

## Abstract (arXiv)

Cover song generation stands out as a popular way of music making in the music-creative community. In this study, we introduce Piano Cover Generation (PiCoGen), a two-stage approach for automatic cover song generation that transcribes the melody line and chord progression of a song given its audio recording, and then uses the resulting lead sheet as the condition to generate a piano cover in the symbolic domain. This approach is advantageous in that it does not required paired data of covers and their original songs for training. Compared to an existing approach that demands such paired data, our evaluation shows that PiCoGen demonstrates competitive or even superior performance across songs of different musical genres.