Research · Audio ML
Guitar Pedal AI
An audio-to-control system that listens to processed guitar and works toward reconstructing an editable effect chain instead of generating an opaque replacement waveform.
Project overview
This final-year Berklee project investigates the inverse problem behind guitar tone: given a wet guitar recording, can a model infer effect structure and controls that a musician can inspect, edit, and reuse? The research evolved through several architectures as I studied where musical content, dry-signal variation, and effect identity become entangled.
In the current direction, DDSP provides the underlying structure for reconstructing delay-line-based effects. The broader aim is to combine machine learning with explicit DSP modules so that the output remains useful inside a producer’s workflow. The final case study will include dataset construction, model iterations, evaluation, and honest failure analysis.
Project demonstration
Guitar Pedal AI Demo
Watch the recorded project demonstration.