# On the Potential of Simple Framewise Approaches to Piano Transcription

arXiv: https://arxiv.org/abs/1612.05153 · HTML: https://mlsanity.com/paper/1612.05153/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Dec 2016.

## Built on by (most cited first, 21 of 21)

- [HEAR: Holistic Evaluation of Audio Representations](https://mlsanity.com/paper/2203.03022/) (2022)
- [MT3: Multi-Task Multitrack Music Transcription](https://mlsanity.com/paper/2111.03017/) (2021)
- [Video2Music: Suitable Music Generation from Videos using an Affective Multimodal Transformer model](https://mlsanity.com/paper/2311.00968/) (2023)
- [Automatic Piano Transcription with Hierarchical Frequency-Time Transformer](https://mlsanity.com/paper/2307.04305/) (2023)
- [Omnizart: A General Toolbox for Automatic Music Transcription](https://mlsanity.com/paper/2106.00497/) (2021)
- [End-to-end Piano Performance-MIDI to Score Conversion with Transformers](https://mlsanity.com/paper/2410.00210/) (2024)
- [A Phoneme-Informed Neural Network Model for Note-Level Singing Transcription](https://mlsanity.com/paper/2304.05917/) (2023)
- [Soft Dynamic Time Warping for Multi-Pitch Estimation and Beyond](https://mlsanity.com/paper/2304.05032/) (2023)
- [Towards Efficient and Real-Time Piano Transcription Using Neural Autoregressive Models](https://mlsanity.com/paper/2404.06818/) (2024)
- [Quantifying the Corpus Bias Problem in Automatic Music Transcription Systems](https://mlsanity.com/paper/2408.04737/) (2024)
- [Machine Learning Techniques in Automatic Music Transcription: A Systematic Survey](https://mlsanity.com/paper/2406.15249/) (2024)
- [Sound and Music Biases in Deep Music Transcription Models: A Systematic Analysis](https://mlsanity.com/paper/2512.14602/) (2025)
- [Polyphonic pitch detection with convolutional recurrent neural networks](https://mlsanity.com/paper/2202.02115/) (2022)
- [Deep-Learning Architectures for Multi-Pitch Estimation: Towards Reliable Evaluation](https://mlsanity.com/paper/2202.09198/) (2022)
- [Pay Attention to the Keys: Visual Piano Transcription Using Transformers](https://mlsanity.com/paper/2411.09037/) (2024)
- [Piano Transcription by Hierarchical Language Modeling with Pretrained Roll-based Encoders](https://mlsanity.com/paper/2501.03038/) (2025)
- [Music Transcription with (Almost) No Supervision](https://mlsanity.com/paper/2605.24193/) (2026)
- [Learning Sparse Analytic Filters for Piano Transcription](https://mlsanity.com/paper/2108.10382/) (2021)
- [From Audio to Symbolic Encoding](https://mlsanity.com/paper/2302.13401/) (2023)
- [A Real-Time Lyrics Alignment System Using Chroma And Phonetic Features For Classical Vocal Performance](https://mlsanity.com/paper/2401.09200/) (2024)
- [Automatic Music Transcription using Convolutional Neural Networks and Constant-Q transform](https://mlsanity.com/paper/2505.04451/) (2025)

## Abstract (arXiv)

In an attempt at exploring the limitations of simple approaches to the task of piano transcription (as usually defined in MIR), we conduct an in-depth analysis of neural network-based framewise transcription. We systematically compare different popular input representations for transcription systems to determine the ones most suitable for use with neural networks. Exploiting recent advances in training techniques and new regularizers, and taking into account hyper-parameter tuning, we show that it is possible, by simple bottom-up frame-wise processing, to obtain a piano transcriber that outperforms the current published state of the art on the publicly available MAPS dataset -- without any complex post-processing steps. Thus, we propose this simple approach as a new baseline for this dataset, for future transcription research to build on and improve.