# Automatic Estimation of Singing Voice Musical Dynamics

arXiv: https://arxiv.org/abs/2410.20540 · HTML: https://mlsanity.com/paper/2410.20540/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Oct 2024.

## Built on by (most cited first, 2 of 2)

- [Joint Estimation of Piano Dynamics and Metrical Structure with a Multi-task Multi-Scale Network](https://mlsanity.com/paper/2510.18190/) (2025)
- [Listening Like a Judge: A Music-Aware Framework for Automatic Singing Performance Evaluation](https://mlsanity.com/paper/2606.26451/) (2026)

## Abstract (arXiv)

Musical dynamics form a core part of expressive singing voice performances. However, automatic analysis of musical dynamics for singing voice has received limited attention partly due to the scarcity of suitable datasets and a lack of clear evaluation frameworks. To address this challenge, we propose a methodology for dataset curation. Employing the proposed methodology, we compile a dataset comprising 509 musical dynamics annotated singing voice performances, aligned with 163 score files, leveraging state-of-the-art source separation and alignment techniques. The scores are sourced from the OpenScore Lieder corpus of romantic-era compositions, widely known for its wealth of expressive annotations. Utilizing the curated dataset, we train a multi-head attention based CNN model with varying window sizes to evaluate the effectiveness of estimating musical dynamics. We explored two distinct perceptually motivated input representations for the model training: log-Mel spectrum and bark-scale based features. For testing, we manually curate another dataset of 25 musical dynamics annotated performances in collaboration with a professional vocalist. We conclude through our experiments that bark-scale based features outperform log-Mel-features for the task of singing voice dynamics prediction. The dataset along with the code is shared publicly for further research on the topic.