# Deep Polyphonic ADSR Piano Note Transcription

arXiv: https://arxiv.org/abs/1906.09165 · HTML: https://mlsanity.com/paper/1906.09165/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Jun 2019.

## Built on by (most cited first, 15 of 15)

- [MT3: Multi-Task Multitrack Music Transcription](https://mlsanity.com/paper/2111.03017/) (2021)
- [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)
- [Scoring Time Intervals using Non-Hierarchical Transformer For Automatic Piano Transcription](https://mlsanity.com/paper/2404.09466/) (2024)
- [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)
- [FretNet: Continuous-Valued Pitch Contour Streaming for Polyphonic Guitar Tablature Transcription](https://mlsanity.com/paper/2212.03023/) (2022)
- [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)
- [MR-MT3: Memory Retaining Multi-Track Music Transcription to Mitigate Instrument Leakage](https://mlsanity.com/paper/2403.10024/) (2024)
- [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)
- [MAJL: A Model-Agnostic Joint Learning Framework for Music Source Separation and Pitch Estimation](https://mlsanity.com/paper/2501.03689/) (2025)
- [Exploring System Adaptations For Minimum Latency Real-Time Piano Transcription](https://mlsanity.com/paper/2509.07586/) (2025)

## Abstract (arXiv)

We investigate a late-fusion approach to piano transcription, combined with a strong temporal prior in the form of a handcrafted Hidden Markov Model (HMM). The network architecture under consideration is compact in terms of its number of parameters and easy to train with gradient descent. The network outputs are fused over time in the final stage to obtain note segmentations, with an HMM whose transition probabilities are chosen based on a model of attack, decay, sustain, release (ADSR) envelopes, commonly used for sound synthesis. The note segments are then subject to a final binary decision rule to reject too weak note segment hypotheses. We obtain state-of-the-art results on the MAPS dataset, and are able to outperform other approaches by a large margin, when predicting complete note regions from onsets to offsets.