eric-culm/reckon

Generate audio dreams

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Run time and cost

This model costs approximately $0.0058 to run on Replicate, or 172 runs per $1, but this varies depending on your inputs. It is also open source and you can run it on your own computer with Docker.

This model runs on CPU hardware. Predictions typically complete within 59 seconds. The predict time for this model varies significantly based on the inputs.

Readme

Reckon

This repository supports Eric Culm’s Reckon, released under Krisis Publishing. The opus is divided in three sections: X, Y and Z. On the one hand, section X an Y are printed in a physical media and contain pre-recorded dreams generated by the Reckon system. On the other hand, section Z transcends the physical reality and takes the spectator inside the virtual brain of Reckon:with this repository you can control its behavior, generating and downloading synthetic dreams.

Feel free to use this code as a creative tool. You are allowed and encouraged to download the dreams generated by Reckon and use them in your own projects. Please, cite me if you do so.

Description

Reckon is an algorithmic system aimed at making tangible, in acoustic terms, the dream of an Artificial Intelligence. The audio information is entirely generated by a digital dreaming apparatus. Inside the latter, the psychic operations of sensorial memories re-elaboration, proper of human dreaming, are substituted with purely mathematical operations between digitalized sensorial stimuli. Reckon exploits the cooperation between two algorithms: a system able to stock and analyze acoustic stimuli to generate acoustic memories and a parallel mechanism that re-elaborates and organizes these memories, building oneiric textures.

On the one hand, a neural network analyzes audio data collections to learn how to reconstruct the essence of the experienced stimuli. The mathematical procedure at the basis of this model, as well as the resulting sound, have much in common with the process through which we are able to mentally recall a perceptual experience, evoking its salient characteristics but omitting its meticulous details. In fact, the sound elements present in Reckon resemble blurred memories, remotely evoking the main characteristics of musical instruments and soundscapes, but discarding their most accurate elements that would make them real.

On the other hand a semi-entropic apparatus controls the neural network and selects, re-elaborates, concatenates and overlaps sound memories to compose digital audio dreams. To perform this operation, the system auto-defines a hierarchy of complex semi-random rules through which create a semantic evolution of the sound material in a similar way to how happens in human biologic dreams.

Usage

With the following parameters you have a broad control on macro details of the generated dreams. Nevertheless, a big part of the process is aleatory, so the output will be always a unpredictable.

  • memories: list of sound memories that a dream or episode can contain. This gives you control over the timbre types that will be included in the sound dream.
  • length: length of the generated sound dream in minutes. The actual duration may be shorter than this setting due to post-processing. Up do one-hour dreams can be generated!
  • depth: this parameter is connected with maximum number of simultaneous sounds that can occur [range 0-1]. Larger values produce fuller mixes.
  • unconsciousness: this parameter sets maximum duration of memories in a dream [range 0-1]. With higher values the sound dreams will contain more diverse segments.
  • output_type: File format of the (downloadable) generated sound dream. Can be wav or mp3.
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