# Semi-Supervised Convolutive NMF for Automatic Piano Transcription

arXiv: https://arxiv.org/abs/2202.04989 · HTML: https://mlsanity.com/paper/2202.04989/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 22 references that are in the map.
- First posted on arXiv 10 Feb 2022.

## Builds on (most cited first, 5 of 5)

- [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)
- [High-resolution Piano Transcription with Pedals by Regressing Onset and Offset Times](https://mlsanity.com/paper/2010.01815/) (2020)
- [On Identifiability of Nonnegative Matrix Factorization](https://mlsanity.com/paper/1709.00614/) (2017)
- [A Provably Correct and Robust Algorithm for Convolutive Nonnegative Matrix Factorization](https://mlsanity.com/paper/1906.06899/) (2019)

## Built on by (most cited first, 1 of 1)

- [A Second-Order Majorant Algorithm for Nonnegative Matrix Factorization](https://mlsanity.com/paper/2303.17992/) (2023)

## Abstract (arXiv)

Automatic Music Transcription, which consists in transforming an audio recording of a musical performance into symbolic format, remains a difficult Music Information Retrieval task. In this work, which focuses on piano transcription, we propose a semi-supervised approach using low-rank matrix factorization techniques, in particular Convolutive Nonnegative Matrix Factorization. In the semi-supervised setting, only a single recording of each individual notes is required. We show on the MAPS dataset that the proposed semi-supervised CNMF method performs better than state-of-the-art low-rank factorization techniques and a little worse than supervised deep learning state-of-the-art methods, while however suffering from generalization issues.