# Pop2Piano : Pop Audio-based Piano Cover Generation

arXiv: https://arxiv.org/abs/2211.00895 · HTML: https://mlsanity.com/paper/2211.00895/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 30 references that are in the map.
- First posted on arXiv 2 Nov 2022.

## Builds on (most cited first, 12 of 12)

- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [Adafactor: Adaptive Learning Rates with Sublinear Memory Cost](https://mlsanity.com/paper/1804.04235/) (2018)
- [Enabling Factorized Piano Music Modeling and Generation with the MAESTRO Dataset](https://mlsanity.com/paper/1810.12247/) (2018)
- [Onsets and Frames: Dual-Objective Piano Transcription](https://mlsanity.com/paper/1710.11153/) (2017)
- [Cutting Music Source Separation Some Slakh: A Dataset to Study the Impact of Training Data Quality and Quantity](https://mlsanity.com/paper/1909.08494/) (2019)
- [POP909: A Pop-song Dataset for Music Arrangement Generation](https://mlsanity.com/paper/2008.07142/) (2020)
- [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)
- [Sequence-to-Sequence Piano Transcription with Transformers](https://mlsanity.com/paper/2107.09142/) (2021)
- [Encoding Musical Style with Transformer Autoencoders](https://mlsanity.com/paper/1912.05537/) (2019)
- [Simultaneous Separation and Transcription of Mixtures with Multiple Polyphonic and Percussive Instruments](https://mlsanity.com/paper/1910.12621/) (2019)
- [Adversarial Learning for Improved Onsets and Frames Music Transcription](https://mlsanity.com/paper/1906.08512/) (2019)

## Built on by (most cited first, 10 of 10)

- [Music Style Transfer with Time-Varying Inversion of Diffusion Models](https://mlsanity.com/paper/2402.13763/) (2024)
- [Controllable Video-to-Music Generation with Multiple Time-Varying Conditions](https://mlsanity.com/paper/2507.20627/) (2025)
- [BandCondiNet: Parallel Transformers-based Conditional Popular Music Generation with Multi-View Features](https://mlsanity.com/paper/2407.10462/) (2024)
- [PIAST: A Multimodal Piano Dataset with Audio, Symbolic and Text](https://mlsanity.com/paper/2411.02551/) (2024)
- [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)
- [Fine-Tuning MIDI-to-Audio Alignment using a Neural Network on Piano Roll and CQT Representations](https://mlsanity.com/paper/2506.22237/) (2025)
- [MPEcho: A Melody and Phoneme-Aware Generative Framework for Controllable Cover Song Generation](https://mlsanity.com/paper/2607.26698/) (2026)
- [Learning Music Style for Piano Arrangement Through Cross-Modal Bootstrapping](https://mlsanity.com/paper/2608.03050/) (2026)

## Abstract (arXiv)

Piano covers of pop music are enjoyed by many people. However, the task of automatically generating piano covers of pop music is still understudied. This is partly due to the lack of synchronized {Pop, Piano Cover} data pairs, which made it challenging to apply the latest data-intensive deep learning-based methods. To leverage the power of the data-driven approach, we make a large amount of paired and synchronized {Pop, Piano Cover} data using an automated pipeline. In this paper, we present Pop2Piano, a Transformer network that generates piano covers given waveforms of pop music. To the best of our knowledge, this is the first model to generate a piano cover directly from pop audio without using melody and chord extraction modules. We show that Pop2Piano, trained with our dataset, is capable of producing plausible piano covers.