Towards Robust Transcription: Exploring Noise Injection Strategies for Training Data Augmentation
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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.
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
10 of 18 references.
References · 10
- 2018Enabling Factorized Piano Music Modeling and Generation with the MAESTRO Datasetcited by 217
- 2017Onsets and Frames: Dual-Objective Piano Transcriptioncited by 94
- 2021MT3: Multi-Task Multitrack Music Transcriptioncited by 71
- 2020High-resolution Piano Transcription with Pedals by Regressing Onset and Offset Timescited by 61
- 2021Noisy Recurrent Neural Networkscited by 42
- 2024A Data-Driven Analysis of Robust Automatic Piano Transcriptioncited by 15
- 2017Invariances and Data Augmentation for Supervised Music Transcriptioncited by 12
- 2020Polyphonic Piano Transcription Using Autoregressive Multi-State Note Modelcited by 11
- 2022HPPNet: Modeling the Harmonic Structure and Pitch Invariance in Piano Transcriptioncited by 7
- 2024Towards Efficient and Real-Time Piano Transcription Using Neural Autoregressive Modelscited by 4
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Citing papers per year: 2025: 1; 2026: 1.
2 papers.