Continuing my inquiry into emerging bioenergy technologies and ecological practices as artifacts of cultural exploration, Microbial Sonorities explores the use of sound to investigate the bioelectric and behavioral patterns of microorganisms. Specifically, it is a hybrid biological-electronic system wherein variations in electrical potential from an array of microbial fuel cells are translated into rhythmic, amplitude and frequency modulations in modular electronic and software-based sound synthesizers.

The research focuses on three primary areas:

Microbial Fuel Cells (MFCs): these are devices that generate electricity from the metabolic reactions of bacteria found in diverse environments such as lakes, wetlands and kitchen waste.

Modular hardware and software synthesizers: The bioelectrical fluctuations of the MFCs are used as modulation and trigger sources for a Eurorack-based modular synthesizer and/or a custom-designed software synthesizer built in Max/MSP or PD. This entails building electronic circuits to amplify the electrical signals generated by the bacteria and software to translate the signals into control voltage (CV) sources appropriate for the synthesizers.

Machine Learning: Machine-learning algorithms are used as a way of interpreting the shifting electrical patterns generated by the bacteria. Pattern recognition/classification is used to trigger synthesizer presets and CV gate signals while statistical regression is used to predict variations in electrical potential. If a comprehensive understanding of the bioelectrical patterns can be attained, it will be used to inform the development of a generative sonic compositional system that is dictated by these patterns. In essence, allowing the bacteria to “express” themselves sonically.

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