AI-based
acoustic testing
Increase quality and reduce costs
Reliable anomaly detection in production and development
Sounce enables automated detection of unwanted noises in real time, for example to detect defects during the assembly process, at end-of-line stations or even on development test benches. The seamless monitoring and testing detects and documents defects that would otherwise remain undetected.
Five steps to AI-based noise detection
1. Listening & Recording
Test bench or station is equipped with minimally invasive sensor technology. Data acquisition is started.
2. Evaluating
The engineer documents the quality criteria of the noise detection via the software and thereby creates the basis of the AI model training.
3. Training
Based on the existing data, a deep learning algorithm is trained and made available in the cloud.
4. Monitoring & Detection
The test bench is continuously monitored and noise anomalies are automatically detected in real time. The noises can be visualised, evaluated and compared.
5. Verify
The engineer provides feedback on the accuracy of the noise detection and optimises the algorithm in the long term.
Usage-based service model based on
Software-as-a-Service solution
The modular cloud infrastructure enables flexible use in various application scenarios with usage-based settlement options.
How does Sounce work?
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