论文标题
东京基金：基于查询的大气数据的生成索
Tokyo Kion-On: Query-Based Generative Sonification of Atmospheric Data
论文作者
论文摘要
在日益增长的环境问题中，数据的交互式显示构成了探索和理解气候变化对地球生态系统完整性的影响的重要工具。本文介绍了东京基恩（Tokyo Kion-On），这是一种基于查询的超声模型，对东京的空气温度从1876年到2021年。该系统使用复发性的神经网络结构，称为LSTM，并在日本旋律的小数据集中训练了注意，并根据所述大气数据进行了调节。在描述了模型的实现之后，介绍了音乐结果的简要比较，并讨论了暴露的超参数如何促进数据的主动和非线性探索。
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.
