aiqbench analysis
This article describes LETHE, a self-referential audio synthesis system implemented in SuperCollider that formally adopts Generative Adversarial Network (GAN) architecture to evolve sound parameters without external datasets. While it utilizes ML terminology like discriminators and optimizers, the primary application is electroacoustic music composition rather than advancing core AI research, products, or quantum technology. Consequently, the use of generative models here is incidental to the artistic goal of sonic exploration, making this piece unsuitable for a publication focused exclusively on AI and quantum advancements. Others
Memory as transformation: LETHE, a self-referential gan-inspired architecture
arXiv:2609.04289v1 Announce Type: new Abstract: LETHE (Latent-parameter Evolution with Temporal Hierarchical quasi-Equilibrium) is a self-referential sonic-oblivion system implemented in SuperCollider. It adopts the formal vocabulary of Generative Adversarial Networks in a closed configuration without external datasets or supervision after initialization. Audio is processed by a 3 x 3 mixing matrix built around two delay lines; its nine coefficients and two delay times evolve through the interaction of a five-feature linear discriminator and a random-perturbation optimizer analogous to single-sample REINFORCE. The discriminator compares current energy behavior with an archive of the initial state and guides parameter updates. Circular, fixed, and live sources can be mixed independently. Across fixed and circular sessions with an ablation control, the active generator is necessary for parametric evolution ($\Delta c_{22}=0.000$ in all 15 ablation sessions). Situated in the tradition of self-referential electroacoustic music, LETHE delegates the sonic outcome to an adaptive closed loop whose parametric space is defined by the composer.
Abstract:LETHE (Latent-parameter Evolution with Temporal Hierarchical quasi-Equilibrium) is a self-referential sonic-oblivion system implemented in SuperCollider. It adopts the formal vocabulary of Generative Adversarial Networks in a closed configuration without external datasets or supervision after initialization. Audio is processed by a 3 x 3 mixing matrix built around two delay lines; its nine coefficients and two delay times evolve through the interaction of a five-feature linear discriminator and a random-perturbation optimizer analogous to single-sample REINFORCE. The discriminator compares current energy behavior with an archive of the initial state and guides parameter updates. Circular, fixed, and live sources can be mixed independently. Across fixed and circular sessions with an ablation control, the active generator is necessary for parametric evolution ($\Delta c_{22}=0.000$ in all 15 ablation sessions). Situated in the tradition of self-referential electroacoustic music, LETHE delegates the sonic outcome to an adaptive closed loop whose parametric space is defined by the composer.
Computation and Language (cs.CL)
arXiv:2609.04289 [cs.CL]
(or arXiv:2609.04289v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2609.04289 Focus to learn more arXiv-issued DOI via DataCite
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