# A holistic approach to polyphonic music transcription with neural networks

arXiv: https://arxiv.org/abs/1910.12086 · HTML: https://mlsanity.com/paper/1910.12086/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Oct 2019.

## Built on by (most cited first, 11 of 11)

- [ReconVAT: A Semi-Supervised Automatic Music Transcription Framework for Low-Resource Real-World Data](https://mlsanity.com/paper/2107.04954/) (2021)
- [Sheet Music Transformer: End-To-End Optical Music Recognition Beyond Monophonic Transcription](https://mlsanity.com/paper/2402.07596/) (2024)
- [Score Transformer: Generating Musical Score from Note-level Representation](https://mlsanity.com/paper/2112.00355/) (2021)
- [End-to-End Real-World Polyphonic Piano Audio-to-Score Transcription with Hierarchical Decoding](https://mlsanity.com/paper/2405.13527/) (2024)
- [A Review and Roadmap of Deep Learning Causal Discovery in Different Variable Paradigms](https://mlsanity.com/paper/2209.06367/) (2022)
- [Late multimodal fusion for image and audio music transcription](https://mlsanity.com/paper/2204.03063/) (2022)
- [A Review and Roadmap of Deep Causal Model from Different Causal Structures and Representations](https://mlsanity.com/paper/2311.00923/) (2023)
- [Real-world Music Plagiarism Detection With Music Segment Transcription System](https://mlsanity.com/paper/2509.08282/) (2025)
- [Disentangling Score Content and Performance Style for Joint Piano Rendering and Transcription](https://mlsanity.com/paper/2509.23878/) (2025)
- [Audio-to-Score Transcription using Pre-trained Features, Data Augmentation, and the New SheetSage-A2S Dataset](https://mlsanity.com/paper/2608.06165/) (2026)
- [TUTTI: Toward generalizable audio-to-score transcription via fully synthesized data](https://mlsanity.com/paper/2609.00640/) (2026)

## Abstract (arXiv)

We present a framework based on neural networks to extract music scores directly from polyphonic audio in an end-to-end fashion. Most previous Automatic Music Transcription (AMT) methods seek a piano-roll representation of the pitches, that can be further transformed into a score by incorporating tempo estimation, beat tracking, key estimation or rhythm quantization. Unlike these methods, our approach generates music notation directly from the input audio in a single stage. For this, we use a Convolutional Recurrent Neural Network (CRNN) with Connectionist Temporal Classification (CTC) loss function which does not require annotated alignments of audio frames with the score rhythmic information. We trained our model using as input Haydn, Mozart, and Beethoven string quartets and Bach chorales synthesized with different tempos and expressive performances. The output is a textual representation of four-voice music scores based on **kern format. Although the proposed approach is evaluated in a simplified scenario, results show that this model can learn to transcribe scores directly from audio signals, opening a promising avenue towards complete AMT.