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Drones & Edge AI Systems
Sound Analysis
Sound is data. Treat it as such.
Every environment produces a continuous soundscape: machinery, voices, traffic, signals, anomalous noises. In most cases, this flow is ignored — or at most listened to by someone. We analyze it with AI models, automatically, scalably and in real time.
From source identification to sound event classification, from anomaly detection to environmental monitoring: we build audio processing pipelines designed for the specific domain.
Problems we solve
Blocked knowledge, critical dependencies and inefficiencies holding back growth
Non-scalable manual audio monitoring
Monitoring environments, plants or public spaces with staff dedicated to listening does not scale.
Sound anomalies not detected in time
An out-of-norm sound contains valuable information. Missing it means losing the opportunity to intervene.
Sound sources difficult to distinguish
In complex environments, multiple sources overlap. Separating the relevant signal from background noise requires specific models.
Compliance on noise and disturbance levels
Monitoring compliance with sound emission limits requires continuous, documented and traceable measurements.
Use Cases
Real projects, measurable results
01
We developed a multi-source audio analysis system for monitoring disturbance levels in urban enviro…
Public administration — Urban acoustic disturbance monitoring
We developed a multi-source audio analysis system for monitoring disturbance levels in urban environments. The system acquires audio from multiple points simultaneously, identifies the origin of detected sounds and classifies events by type and intensity, producing a continuous georeferenced map of the soundscape.
Operational tool for public disturbance management and verification of compliance with sound emission limits.
02
Automatic classification of sound events in complex environments: recognition of recurring patterns…
Behavioral and environmental analysis from audio
Automatic classification of sound events in complex environments: recognition of recurring patterns, detection of anomalies from baseline, identification of specific sources in the presence of background noise.
The system learns what is normal. It signals when something is not.
Technology Corner
The architecture and technologies that make the difference
Our projects don't rely on off-the-shelf solutions. Every system is designed on the right architecture for the specific problem — and the technology choice makes the difference between a tool that works in demo and one that holds up in production.
Acquisition, preprocessing and models
- ✓ Acquisition pipeline from multiple sourcesmicrophones, arrays, environmental sensors
- ✓ Sound event classification with CNN on spectrograms
- ✓ Sound source separationto isolate sources in environments with overlapping signals
Architecture and georeferencing
- ✓ Architectures optimized for real-time processingwith low latency
- ✓ Edge infrastructure deploymentfor on-site analysis without cloud connectivity dependency
- ✓ Triangulation and spatial localizationgeoreferenced output for GIS system integration
Interested in this service?
Contact us for a free consultation and find out how we can help you.
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