What is Wi-Fi, ontologically speaking? The Gigahertz electromagnetic spectrum exists irrespective of human or technological intervention, but when the space is co-opted by human and technical actors, does it fundamentally change?
What about the human and technical actors? When an assemblage of electronic components are organized in a way meant to transfer information along this electromagnetic band, does the technology transcend its own capabilities? Do humans, once plugged into this stream become more than what people in Shakespeare’s time would have considered human? Wielding the sum total of mankind’s knowledge in an internet connected, handheld device, do we become superhuman conjurers, magicians? If this is the case then can we consider Wi-Fi a demon or more accurately a daimon from the Greek definition denoting a demigod acting as a personal assistant to humans who are otherwise blind to unseen forces like electromagnetism? If Wi-Fi could communicate with us directly, what would it say? How would it sound? And how could we respond?
During the fall 2023 semester, I will delve into the last two questions. Having created a toolchain, I will set out to coax interesting if not arresting sounds from the 2.4GHz spectrum. As I iterate over the toolchain, I will explore sounds along a spectrum ranging from as raw as possible to heavily synthesized and effect-laden. I will consider these ends of the spectrum to represent how Wi-Fi “sounds” in its most pure form one the one hand and how the basic information might be used to create something we might recognize as musical on the other hand
Toolchain
HackRF One is a half-duplex, software defined radio transceiver. It is the piece of hardware that enables receiving and demodulating signals in the Wi-Fi band.
GQRX is an open source graphical user interface (GUI) that allows a person (or a piece of code) to tune, demodulate, and otherwise control the radio transceiver.
SuperCollider is an open source environment and programming language often used in musical synthesis and algorithmic composition cases. Here it will be used as a possible bridge from the raw Wi-Fi sounds of the transceiver to the human receiver’s auditory cortex.
Reinforcement Learning (RL) is a machine learning technique that enables a piece of software to achieve preset goals in an undetermined way based on a policy set by the programmer. In this case, upon finding interesting parts of the Wi-Fi band, I will begin to develop a policy to allow the program to listen and tune into those aspects of the band that I find interesting. A large part of the work with RL is finding a good policy or set of rules for the program to follow. This search for an optimal policy is well suited to the iterative nature of the Sound Art Project course.