to the sooe

to the sooe, 2018. Photo credit: Alexandre Saunier.
to the sooe
 / deep learning sound work
 / 2018

to the sooe is a sound object that houses a human voice murmuring the words of a neural network trained by a deceased author.

Description

to the sooe is a sound object that features a binaural recording of Erin Gee’s voice as she re-articulates the murmurs of a machine learning algorithm learning to speak. Through this work, the artists re-embody the cognitive processes and creative voices of three agents (a deceased author, a deep learning neural net, and an ASMR performer) into a tangible device. These human and nonhuman agencies are materialized in the object through speaking and writing: a disembodied human voice, words etched onto a mirrored, acrylic surface, as well as code written into the device’s silicon memory.

The algorithmic process used in this work is a deep recurrent neural network agent known as “long short term memory” (LSTM). The algorithm “reads” Emily Brontë’s Wuthering Heights character by character, familiarizing itself with the syntactical universe of the text. As it reads and re-reads the book, it attempts to mimic Brontë’s style within the constraints of its own artificial “body”, hence finding its own alien voice.

The reading of this AI-generated text by a human speaker allows the listener to experience simultaneously the neural network agent’s linguistic journey as well as the augmentation of this speech through vocalization techniques adapted from Autonomous Sensory Meridian Response (ASMR). ASMR involves the use of acoustic “triggers” such as gentle whispering, fingers scratching or tapping, in an attempt to induce tingling sensations and pleasurable auditory-tactile synaesthesia in the user. Through these autonomous physiological experiences, the artists hope to reveal the autonomous nature of the listener’s own body, implying the listener as an already-cyborgian aspect of the hybrid system in place.

Credits

Authors: Sofian Audry and Erin Gee

Neural network programming and training: Sofian Audry
Vocal performer, audio recording and editing, electronics: Erin Gee

3D printing design and laser etching: Grégory Perrin