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

这项研究将探索递归神经网络数据的可听化，作为产生新的、吸引人的音乐材料的一种方式，创建一个电声作品组合，可以提供对这些网络如何运行的一些见解，包括数据转换的速度和递归细胞对该转换的影响。我希望这项工作能够将人类控制的潜力集中到深度学习网络的使用和设计中，用于创造性目的，以及算法发声。研究问题--递归神经网络的操作数据如何能够令人信服地映射为音频信息以供收听？--从RNN获得的不同数据流的研究问题，这些数据流可以用来生成新的、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内在地联系在一起？-如何使用这种发声输出来创建空间化的声音艺术装置组合，为非专业观众提供RNN操作方式的洞察？

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?