AI Audio Pipelines and Real-Time DSP
Real-time digital signal processing (DSP) development, from simple EQs to complex processing chains. We also integrate AI and ML: stem separation, voice synthesis, transcription, and more. Trained in PyTorch, exported to ONNX, deployed on the user's device or in the cloud.
Why AI Audio Is Different
A model in a notebook isn't a product. The hard part is real time: deployable, protected and stable under load, on constrained hardware, or sample-locked across a network. That's the work Edge Audio Labs does.
What Edge Audio Labs Builds
Real-time DSP
Low-latency engines: effects, metering, mixing.
Model training
Built or reworked in PyTorch for the audio task.
On-device deployment
ONNX or LibTorch, running offline inside a plugin or app.
Cloud inference
Elastic rendering behind an API, when scale matters.
IP protection
Encrypted, device-bound models the engine can't be copied from.
Hardware, embedded and Dante
Firmware, control surfaces and AoIP, sample-locked across devices.
The Stack
