← 研踪 PaperTrailarXiv:2611.07210 · v1
Latent Terrain: Adapting Neural Audio Autoencoders as Design Materials in NIME
首次提交 2026-11-05 06:58
版本更新 2026-11-05 06:58
雷达观测 2026-11-06 10:04
摘要
Neural audio autoencoders, a deep learning method for sound synthesis, are increasingly popular in AI-enhanced NIMEs. It is timely to explore as a community how this technological opportunity has opened a domain of design in NIME. This paper focuses on one compelling technique of using autoencoders for sound synthesis: navigating their latent space as a generative sound space. We introduce Latent Terrain, a Max/MSP tool package designed to tailor latent spaces into corpus-based sound spaces for NIMEs. We describe the rationale and development process of Latent Terrain to offer insights into the use of autoencoders in a material-oriented crafting space of musical interface design. We deliver an annotated portfolio resulting from a collaborative artistic exploration of Latent Terrain with four NIME makers, to showcase the design possibilities opened by autoencoders. We reflect on our practice-based account to discuss the challenges and opportunities of enabling neural audio autoencoders as design materials for AI-enhanced NIMEs.
作者备注：In Proceedings of the International Conference on New Interfaces for Musical Expression 2026
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v1 · 2026-11-05 06:58 · 最新观测版本
Latent Terrain: Adapting Neural Audio Autoencoders as Design Materials in NIME
Neural audio autoencoders, a deep learning method for sound synthesis, are increasingly popular in AI-enhanced NIMEs. It is timely to explore as a community how this technological opportunity has opened a domain of design in NIME. This paper focuses on one compelling technique of using autoencoders for sound synthesis: navigating their latent space as a generative sound space. We introduce Latent Terrain, a Max/MSP tool package designed to tailor latent spaces into corpus-based sound spaces for NIMEs. We describe the rationale and development process of Latent Terrain to offer insights into the use of autoencoders in a material-oriented crafting space of musical interface design. We deliver an annotated portfolio resulting from a collaborative artistic exploration of Latent Terrain with four NIME makers, to showcase the design possibilities opened by autoencoders. We reflect on our practice-based account to discuss the challenges and opportunities of enabling neural audio autoencoders as design materials for AI-enhanced NIMEs.
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