Sound Design Strategies for Latent Audio Space Explorations using Deep Learning Architectures
Paper i proceeding, 2023

The research in Deep Learning applications in sound and music computing have gathered an interest in the recent years; however, there is still a missing link between these new technologies and on how they can be incorporated into real-world artistic practices. In this work, we explore a well-known Deep Learning architecture called Variational Autoencoders (VAEs). These architectures have been used in many areas for generating latent spaces where data points are organized so that similar data points locate closer to each other. Previously, VAEs have been used for generating latent timbre spaces or latent spaces of symbolic music excepts. Applying VAE to audio features of timbre requires a vocoder to transform the timbre generated by the network to an audio signal, which is computationally expensive. In this work, we apply VAEs to raw audio data directly while bypassing audio feature extraction. This approach allows the practitioners to use any audio recording while giving flexibility and control over the aesthetics through dataset curation. The lower computation time in audio signal generation allows the raw audio approach to be incorporated into real-time applications. In this work, we propose three strategies to explore latent spaces of audio and timbre for sound design applications. By doing so, our aim is to initiate a conversation on artistic approaches and strategies to utilize latent audio spaces in sound and music practices.

Författare

Kivanc Tatar

Chalmers, Data- och informationsteknik, Interaktionsdesign och Software Engineering

Kelsey Cotton

Chalmers, Data- och informationsteknik, Interaktionsdesign och Software Engineering

Daniel Bisig

Zurich University of the Arts

Proceedings of the Sound and Music Computing Conferences

25183672 (eISSN)

Vol. 2023-June 239-246
9789152773727 (ISBN)

20th Sound and Music Computing Conference, SMC 2023
Hybrid, Stockholm, Sweden,

Ämneskategorier

Övrig annan humaniora

Arkitektur

Datavetenskap (datalogi)

Mer information

Senast uppdaterat

2024-02-16