# Towards Robust Transcription: Exploring Noise Injection Strategies for Training Data Augmentation

arXiv: https://arxiv.org/abs/2410.14122 · HTML: https://mlsanity.com/paper/2410.14122/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 18 references that are in the map.
- First posted on arXiv 18 Oct 2024.

## Builds on (most cited first, 10 of 10)

- [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)
- [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)
- [Noisy Recurrent Neural Networks](https://mlsanity.com/paper/2102.04877/) (2021)
- [A Data-Driven Analysis of Robust Automatic Piano Transcription](https://mlsanity.com/paper/2402.01424/) (2024)
- [Invariances and Data Augmentation for Supervised Music Transcription](https://mlsanity.com/paper/1711.04845/) (2017)
- [Polyphonic Piano Transcription Using Autoregressive Multi-State Note Model](https://mlsanity.com/paper/2010.01104/) (2020)
- [HPPNet: Modeling the Harmonic Structure and Pitch Invariance in Piano Transcription](https://mlsanity.com/paper/2208.14339/) (2022)
- [Towards Efficient and Real-Time Piano Transcription Using Neural Autoregressive Models](https://mlsanity.com/paper/2404.06818/) (2024)

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

- [PianoVAM: A Multimodal Piano Performance Dataset](https://mlsanity.com/paper/2509.08800/) (2025)
- [Text-Anchored Semantic Perturbations for Transferable Jailbreak Attacks on Multimodal Large Language Models](https://mlsanity.com/paper/2608.22312/) (2026)

## Abstract (arXiv)

Recent advancements in Automatic Piano Transcription (APT) have significantly improved system performance, but the impact of noisy environments on the system performance remains largely unexplored. This study investigates the impact of white noise at various Signal-to-Noise Ratio (SNR) levels on state-of-the-art APT models and evaluates the performance of the Onsets and Frames model when trained on noise-augmented data. We hope this research provides valuable insights as preliminary work toward developing transcription models that maintain consistent performance across a range of acoustic conditions.