AudioInspector AI — next-level machine-learning quality assessment
AudioInspector has featured machine-learning quality assessment since 2006 — so far, the models were trained externally by us and delivered the overall quality assessment. AudioInspector AI (AIAI) takes it to the next level: you can now run training yourself — and train not only the overall assessment, but also defects. AIAI learns what good and degraded audio looks like in your archive, from your own, verified AudioInspector analysis results.
What it does
- New: run your own training — for the overall quality assessment and for defects.
- Shows the traditional algorithmic detections exactly as before — the production system remains stable and predictable.
- Adds an AI quality suggestion for every file, which the inspector confirms or overrules.
- Marks the regions of a recording that influenced the decision (“AI Attention”) — so inspectors listen where it matters.
- Learns which artefacts matter most for the final quality — including patterns classical algorithms cannot see.
AI Attention marks in the detection log and waveform: the model points the inspector to the passages that determined its quality suggestion.
Your data stays yours
- Models are trained separately from the production system.
- Optional local training: no internet access required, runs on your own infrastructure.
- Your audio and your verified results never have to leave your house.
Getting started
An AI model is as good as the data it learns from. AIAI is therefore trained with your material: existing, verified AudioInspector or BatchInspector analysis results from your archive. The more representative your verified results are, the better the model reflects your house standards.
You don’t have to figure this out alone: depending on your use case, we advise you on how to create suitable training samples and accompany the training on a project basis — from data preparation to a model that matches your quality standards.
Contact us at contact@audioinspector.com.