TY  - CPAPER
U1  - Konferenzveröffentlichung
A1  - Bisig, Daniel
A1  - Wegner, Ephraim
ED  - Soddu, Celestino
ED  - Colabella, Enrica
T1  - Deep Dream for Sound Synthesis
T2  - XXVI Generative Art Conference - GA2023
N2  - In 2015, Google engineer Alexander Mordvintsev presented DeepDream as technique to visualise the feature analysis capabilities of deep neural networks that have been trained on image classification tasks. For a brief moment, this technique enjoyed some popularity among scientists, artists, and the general public because of its capability to create seemingly hallucinatory synthetic images. But soon after, research moved on to generative models capable of producing more diverse and more realistic synthetic images. At the same time, the means of interaction with these models have shifted away from a direct manipulation of algorithmic properties towards a predominance of high level controls that obscure the model's internal working. In this paper, we present research that returns to DeepDream to assess its suit-ability as method for sound synthesis. We consider this research to be necessary for two reasons: it tackles a perceived lack of research on musical applications of DeepDream, and it addresses DeepDream's potential to combine data driven and algorithmic approaches. Our research includes a study of how the model architecture, choice of audio data-sets, and method of audio processing influence the acoustic characteristics of the synthesised sounds. We also look into the potential application of DeepDream in a live-performance setting. For this reason, the study limits itself to models consisting of small neural networks that process time-domain representations of audio. These models are resource-friendly enough to operate in real time. We hope that the results obtained so far highlight the attractiveness of Deep-Dream for musical approaches that combine algorithmic investigation with curiosity driven and open ended exploration.
KW  - Sound Synthesis
KW  - Deep Learning
Y1  - 2023
UR  - https://artscience-ebookshop.com/GA2023_E-Book.pdf
SN  - 978-88-96610-45-9
SB  - 978-88-96610-45-9
SP  - 82
EP  - 96
PB  - Domus Argenia Publisher
CY  - Rom
ER  -