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04 / AI

Audio & signals

Measurable intelligence, in the real world

Recognise events and states from audio or sensor signals, with signal processing and inference evaluated on microcontrollers where appropriate.

Platforms and tools follow your context. Some examples:

Python / NumPy / C++ / TinyML / Computer vision / Edge AI

AI

Audio & signals

Data from audio, sensors and time-varying signals

  • Signal acquisition

    Audio · sensors · samplingSources and measurement conditions

  • Preprocessing

    Windows · filters · normalisationSignal preparation

  • Features

    Spectra · statistics · trendsInformation from the signal

  • Models & classification

    Events · states · anomaliesGoals defined for the real case

  • Inference

    PCs · edge · microcontrollersAvailable memory and timing

  • Measurements & validation

    Noise · variability · latencyVerification in realistic conditions

The technologies shown are examples.

Engineering support within your team, with shared tools and working methods.

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TECHNICAL SCOPE

Audio analysis and signal classification

Sampling, noise and time windows affect what a model can recognise. Work covers acquisition, preprocessing and feature selection before classification. Tests separate recordings and capture conditions so near-identical segments do not appear in both training and evaluation.

Activities and verification

  • Define sampling, filters, windows and spectral or temporal features.
  • Compare classes, background noise and unknown events.
  • Measure recognition delay, memory and load on the target system.

Initial decisions and integration

I agree events to recognise, sensor placement, capture frequency and time available for decisions. I can contribute to signal processing or classification within the client’s stack, documenting formats, preprocessing and the connection to application logic.

A verification example

Recordings and capture conditions are separated between development and testing, including noise and signals outside the expected classes. Delay is measured across the complete chain, from the acquisition window to the result, beyond model execution alone.

Deliverables and acceptance criteria

Processing chain, model, implementation and report using separate data. The catalogue’s bare-metal audio classifier illustrates a possible path to C inference without an operating system.

Where to start

Provide representative recordings, sample rate, events to distinguish and device constraints.

Can classification run on the device?

This is assessed on the target with the intended signal, considering acquisition, preprocessing, available memory and computation. The model and runtime follow those constraints.

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Projects in this area

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THE NEXT STEP

A system ready
for its next step?

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