# A Minimal Template for Interactive Web-based Demonstrations of Musical Machine Learning

arXiv: https://arxiv.org/abs/1902.03722 · HTML: https://mlsanity.com/paper/1902.03722/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Feb 2019.

## Built on by (most cited first, 4 of 4)

- [The Ethical Implications of Generative Audio Models: A Systematic Literature Review](https://mlsanity.com/paper/2307.05527/) (2023)
- [JukeDrummer: Conditional Beat-aware Audio-domain Drum Accompaniment Generation via Transformer VQ-VAE](https://mlsanity.com/paper/2210.06007/) (2022)
- [Calliope: An Online Generative Music System for Symbolic Multi-Track Composition](https://mlsanity.com/paper/2504.14058/) (2025)
- [Apollo: An Interactive Environment for Generating Symbolic Musical Phrases using Corpus-based Style Imitation](https://mlsanity.com/paper/2504.14055/) (2025)

## Abstract (arXiv)

New machine learning algorithms are being developed to solve problems in different areas, including music. Intuitive, accessible, and understandable demonstrations of the newly built models could help attract the attention of people from different disciplines and evoke discussions. However, we notice that it has not been a common practice for researchers working on musical machine learning to demonstrate their models in an interactive way. To address this issue, we present in this paper an template that is specifically designed to demonstrate symbolic musical machine learning models on the web. The template comes with a small codebase, is open source, and is meant to be easy to use by any practitioners to implement their own demonstrations. Moreover, its modular design facilitates the reuse of the musical components and accelerates the implementation. We use the template to build interactive demonstrations of four exemplary music generation models. We show that the built-in interactivity and real-time audio rendering of the browser make the demonstration easier to understand and to play with. It also helps researchers to gain insights into different models and to A/B test them.