GANSpaceSynth: A Hybrid Generative Adversarial Network Architecture for Organising the Latent Space using a Dimensionality Reduction for Real-Time Audio Synthesis

Loading...
Thumbnail Image
Journal Title
Journal ISSN
Volume Title
A4 Artikkeli konferenssijulkaisussa
This publication is imported from Aalto University research portal.
View publication in the Research portal
View/Open full text file from the Research portal
Date
2021-07-19
Major/Subject
Mcode
Degree programme
Language
en
Pages
10
Series
Abstract
Generative models enable possibilities in audio domain to present timbre as vectors in a high-dimensional latent space with Gen- erative Adversarial Networks (GANs). It is a common method in GAN models in which the musician’s control over timbre is mostly limited to sampling random points from the space and interpolating between them. In this paper, we present a novel hybrid GAN architecture that allows musicians to explore the GAN latent space in a more controlled manner, identifying the audio features in the trained checkpoints and giving an opportunity to specify particular audio features to be present or absent in the generated audio samples. We extend the paper with the detailed description of our GANSpaceSynth and present the Hallu composition tool as an application of this hybrid method in computer music practices.
Description
Keywords
GANSpaceSynth, AI-terity, Artificial Intelligence (AI), Deep Learning, new interfaces for musical expression, Digital musical instruments, Composition, Performance
Citation
Tahiroğlu , K , Kastemaa , M & Koli , O 2021 , ' GANSpaceSynth: A Hybrid Generative Adversarial Network Architecture for Organising the Latent Space using a Dimensionality Reduction for Real-Time Audio Synthesis ' , Paper presented at Conference on AI Music Creativity , Graz , Austria , 18/07/2021 - 22/07/2021 pp. 10 . https://doi.org/10.5281/zenodo.5137902