Bringing a Speech-Enhancement Neural Network to Native C

The challenge

An AI audio startup had a speech-enhancement model that cleans up voices in noisy environments. It ran in a research environment, but to demonstrate it on real devices, and eventually ship it, the model needed to run natively and continuously without a Python runtime.

What we did

We ported the neural network to portable native C, implementing each layer to match the original model's behavior, and built the signal-processing stages needed to process audio continuously in real time. Comparison tools checked intermediate results against the reference model at every stage of the port. We integrated the native implementation into a mobile demo app and explored options for hardware acceleration on mobile processors. See our advanced algorithms and embedded software services.

The result

The native implementation supported the client's investor demo, and the technology was handed over to the startup's in-house team to continue development.

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