Edge-AI Audio Analysis
DOMAINAudio analysis · ML inference
WHEREOn-device / at the edge
TARGETEmbedded (MCU / Zynq — TBD)
STATUSPlanned — to build
The idea
Run audio analysis where the audio actually is. Instead of streaming a sound stream to a server to be classified, this project puts a trained neural model directly on embedded hardware so the device itself can detect or classify what it hears — keeping latency low, working offline, and never sending raw audio off-device.
Why this one
It sits at the intersection of two things I already work in: real-time audio on embedded targets, and deploying ML to constrained hardware.
Planned goals
- Pick a concrete audio task (e.g. sound-event detection / acoustic anomaly / keyword spotting) and a dataset.
- Train and quantize a small model, then convert it for on-device inference (TFLite / TFLite Micro).
- Deploy to embedded hardware and measure the real numbers — latency, memory, accuracy after quantization.
- Build the audio front end (capture → features → inference) as a clean, real-time pipeline.
- Document it as a build-in-public series, the same way as AuraScope.
Scope note: this is the next project on the bench — the specific task, model, and target board will be pinned down as it kicks off. The framing here is the direction, not a finished spec.
Status
Planned. Step-by-step write-ups will appear here as the build starts.