Portfolio of compositions: Creating electroacoustic works through the sonification of recursive neural networks, and exploring the creative use of in

作品组合：通过递归神经网络的发声创作电声作品，并探索in的创造性运用

• 批准号: 2886370
• 金额: --
• 依托单位: University of Manchester
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2886370
• 关键词: Portfolio; compositions; Creating; electroacoustic; works

This research will explore the sonification of data from recursive neural networks as a way to generate new, engaging musical materials, to create a portfolio of electroacoustic compositions that can provide some insights into how these networks operate, including the rate of data transformation and the effect of recurrent cells on that transformation. I hope this work can bring into focus the potential for human control in the use and design of deep learning networks for creative purposes, and in algorithmic sonification.Research questions- How can recursive neural networks' operating data be convincingly mapped as audio information to be listened to?- Of the different data streams obtainable from an RNN, which can be used to generate new, interesting musical materials and structures that are intrinsically linked to the networks' algorithmic processes?- How can this sonified output be used to create a portfolio of spatialised sound-art installations that provide insights into the way RNNs operate to a non-expert audience?

这项研究将探索递归神经网络数据的声音化，作为生成新的、引人入胜的音乐材料的一种方式，以创建一个电声作品组合，可以提供一些关于这些网络如何运作的见解，包括数据转换的速率和效果循环细胞对该转化的影响。我希望这项工作能够使人类控制在出于创造性目的的深度学习网络的使用和设计以及算法可听化方面的潜力得到关注。研究问题-如何将递归神经网络的操作数据令人信服地映射为音频信息听过？- 从 RNN 获得的不同数据流中，这些数据流可用于生成新的、有趣的音乐材料和结构，这些材料和结构与网络的算法过程有内在联系？- 如何使用这种可听化的输出来创建一个组合的空间化的声音艺术装置可以让非专业观众深入了解 RNN 的运作方式？