# Tokyo Kion-On: Query-Based Generative Sonification of Atmospheric Data

arXiv: https://arxiv.org/abs/2208.02494 · HTML: https://mlsanity.com/paper/2208.02494/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 27 references that are in the map.
- First posted on arXiv 4 Aug 2022.

## Builds on (most cited first, 4 of 4)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Music Transformer](https://mlsanity.com/paper/1809.04281/) (2018)
- [Coupled Recurrent Models for Polyphonic Music Composition](https://mlsanity.com/paper/1811.08045/) (2018)
- [Sonification Abstraite/Sonification Concr`ete: An 'Aesthetic Perspective Space' for Classifying Auditory Displays in the Ars Musica Domain](https://mlsanity.com/paper/1311.5426/) (2013)

## Abstract (arXiv)

Amid growing environmental concerns, interactive displays of data constitute an important tool for exploring and understanding the impact of climate change on the planet's ecosystemic integrity. This paper presents Tokyo kion-on, a query-based sonification model of Tokyo's air temperature from 1876 to 2021. The system uses a recurrent neural network architecture known as LSTM with attention trained on a small dataset of Japanese melodies and conditioned upon said atmospheric data. After describing the model's implementation, a brief comparative illustration of the musical results is presented, along with a discussion on how the exposed hyper-parameters can promote active and non-linear exploration of the data.