# Adversarial Learning for Improved Onsets and Frames Music Transcription

arXiv: https://arxiv.org/abs/1906.08512 · HTML: https://mlsanity.com/paper/1906.08512/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Jun 2019.

## Built on by (most cited first, 13 of 13)

- [Sequence-to-Sequence Piano Transcription with Transformers](https://mlsanity.com/paper/2107.09142/) (2021)
- [Automatic Piano Transcription with Hierarchical Frequency-Time Transformer](https://mlsanity.com/paper/2307.04305/) (2023)
- [ReconVAT: A Semi-Supervised Automatic Music Transcription Framework for Low-Resource Real-World Data](https://mlsanity.com/paper/2107.04954/) (2021)
- [Pop2Piano : Pop Audio-based Piano Cover Generation](https://mlsanity.com/paper/2211.00895/) (2022)
- [Jointist: Joint Learning for Multi-instrument Transcription and Its Applications](https://mlsanity.com/paper/2206.10805/) (2022)
- [Music Foundation Model as Generic Booster for Music Downstream Tasks](https://mlsanity.com/paper/2411.01135/) (2024)
- [Towards Efficient and Real-Time Piano Transcription Using Neural Autoregressive Models](https://mlsanity.com/paper/2404.06818/) (2024)
- [Jointist: Simultaneous Improvement of Multi-instrument Transcription and Music Source Separation via Joint Training](https://mlsanity.com/paper/2302.00286/) (2023)
- [AMT-APC: Automatic Piano Cover by Fine-Tuning an Automatic Music Transcription Model](https://mlsanity.com/paper/2409.14086/) (2024)
- [Learning Sparse Analytic Filters for Piano Transcription](https://mlsanity.com/paper/2108.10382/) (2021)
- [Learning source-aware representations of music in a discrete latent space](https://mlsanity.com/paper/2111.13321/) (2021)
- [MAJL: A Model-Agnostic Joint Learning Framework for Music Source Separation and Pitch Estimation](https://mlsanity.com/paper/2501.03689/) (2025)
- [Disentangling Score Content and Performance Style for Joint Piano Rendering and Transcription](https://mlsanity.com/paper/2509.23878/) (2025)

## Abstract (arXiv)

Automatic music transcription is considered to be one of the hardest problems in music information retrieval, yet recent deep learning approaches have achieved substantial improvements on transcription performance. These approaches commonly employ supervised learning models that predict various time-frequency representations, by minimizing element-wise losses such as the cross entropy function. However, applying the loss in this manner assumes conditional independence of each label given the input, and thus cannot accurately express inter-label dependencies. To address this issue, we introduce an adversarial training scheme that operates directly on the time-frequency representations and makes the output distribution closer to the ground-truth. Through adversarial learning, we achieve a consistent improvement in both frame-level and note-level metrics over Onsets and Frames, a state-of-the-art music transcription model. Our results show that adversarial learning can significantly reduce the error rate while increasing the confidence of the model estimations. Our approach is generic and applicable to any transcription model based on multi-label predictions, which are very common in music signal analysis.