Fundamental Resonance is a broadcast series exploring a new approach to the audition of acoustic,  mechanical,  and electromagnetic vibrations.   Each episode combines an audio essay with a soundscape,  composed of examples of the phenomena discussed,  as a way to explore the unreachable,  reveal the hidden,  and manifest what lies beyond the senses.


Fundamental Resonance is an audio supplement to the art exhibition
Energy Fields: Vibrations of the Pacific
Co-presented by Fulcrum Arts and Chapman University
September 15, 2024 - January 19, 2024
as  part of PST ART

SUNDAY OCTOBER 6, 2024

Internal Medicine: Anechoic Chamber and Medical Sonification





This program delves into the auditory landscape of the human body, examining the sounds of the body experienced within an anechoic chamber with the outputs of medical devices.

In an anechoic chamber—a room meticulously designed to eliminate all echoes and external noise—external silence becomes an almost physical force and the inner sounds of the working body emerge: the steady thump of a heartbeat, the subtle rush of blood through veins, and even the faint hum of the nervous system.

Medical technologies used to translate the body's hidden vibrations into audible data also serve to make the inaudible audible.  Ultrasound, EEG, MRI, and stethoscopes reveal the intricate workings of the human body, transforming subtle physiological processes into sonic diagnoses.

Sourced sounds: Anechoic chamber recreation based on personal experience inside the USC MEMS group Anechoic Chamber for Acoustic Testing.  

Medical device recordings from the following sources:
                                                         
                                                              MIT-BIH Arrhythmia Database    
                                                              Respiratory Sound Database    
                                                             Coswara
                                                     
                                                            UC Irvine Machine Learning Repository


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