Reading what happens underwater
The underwater environment is difficult to observe: visibility is limited, conditions change quickly and acoustic data is often noisy, incomplete or hard to interpret.
We combine signal analysis, machine learning and custom software to identify acoustic patterns, follow objects or phenomena over time and support decisions based on sonar data.
What we do
From signal cleanup to operational visualization, we build pipelines and tools adapted to the context.
Sonar signal analysis
We process active and passive sonar data, filtering noise, interference and environmental distortion.
Acoustic recognition
We classify events, sound sources, anomalies and recurring patterns inside underwater recordings.
Underwater tracking
We track objects, signals or phenomena over time by integrating acoustic, temporal and spatial data.
Data fusion
We combine sonar data with environmental sensors, positioning, bathymetric maps and operational data.
Dashboards and visualization
We build interfaces to explore signals, trajectories, anomalies and underwater scenarios.
Analysis automation
We reduce manual work on large acoustic datasets with automated pipelines and alerting systems.
Where it can be applied
From raw signal to useful information
Data understanding
We analyze sonar type, data format, operating conditions and project objectives.
Preprocessing
We apply filtering, normalization, segmentation and noise reduction techniques.
Modeling
We develop signal processing, machine learning or deep learning algorithms depending on the problem.
Tracking and interpretation
We reconstruct sequences, trajectories or significant events over time.
Software and integration
We turn models into dashboards, APIs, data pipelines or integrable modules.
Technology to observe, monitor and understand
Our work focuses on data analysis, underwater environmental monitoring and software tools for interpreting complex signals in civil, scientific and industrial contexts.