# Learning To Generate Piano Music With Sustain Pedals

arXiv: https://arxiv.org/abs/2111.01216 · HTML: https://mlsanity.com/paper/2111.01216/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 8 references that are in the map.
- First posted on arXiv 1 Nov 2021.

## Builds on (most cited first, 6 of 6)

- [Enabling Factorized Piano Music Modeling and Generation with the MAESTRO Dataset](https://mlsanity.com/paper/1810.12247/) (2018)
- [Music Transformer](https://mlsanity.com/paper/1809.04281/) (2018)
- [Compound Word Transformer: Learning to Compose Full-Song Music over Dynamic Directed Hypergraphs](https://mlsanity.com/paper/2101.02402/) (2021)
- [High-resolution Piano Transcription with Pedals by Regressing Onset and Offset Times](https://mlsanity.com/paper/2010.01815/) (2020)
- [EMOPIA: A Multi-Modal Pop Piano Dataset For Emotion Recognition and Emotion-based Music Generation](https://mlsanity.com/paper/2108.01374/) (2021)
- [Transfer Learning for Piano Sustain-Pedal Detection](https://mlsanity.com/paper/2103.13219/) (2021)

## Built on by (most cited first, 2 of 2)

- [Impact of time and note duration tokenizations on deep learning symbolic music modeling](https://mlsanity.com/paper/2310.08497/) (2023)
- [Modeling Bends in Popular Music Guitar Tablatures](https://mlsanity.com/paper/2308.12307/) (2023)

## Abstract (arXiv)

Recent years have witnessed a growing interest in research related to the detection of piano pedals from audio signals in the music information retrieval community. However, to our best knowledge, recent generative models for symbolic music have rarely taken piano pedals into account. In this work, we employ the transcription model proposed by Kong et al. to get pedal information from the audio recordings of piano performance in the AILabs1k7 dataset, and then modify the Compound Word Transformer proposed by Hsiao et al. to build a Transformer decoder that generates pedal-related tokens along with other musical tokens. While the work is done by using inferred sustain pedal information as training data, the result shows hope for further improvement and the importance of the involvement of sustain pedal in tasks of piano performance generations.