Specialist acoustic data processing and analysis
We work with fisheries, offshore, and research organisations to process large acoustic datasets into clear, usable outputs. From raw echosounder data through to final results, we provide practical analysis, interpretation, and workflow support — without the need for in-house expertise. Where required, outputs are integrated with environmental and ecological datasets to support modelling, assessment, and decision-making.
Acoustic data processing
Processing of single-beam, multibeam, imaging sonar, and broadband echosounder data into structured, analysis-ready outputs.
This includes noise removal and data cleaning, gridding and standardisation, feature extraction (schools, swarms, layers), and calculation of summary statistics and biomass.
Work also covers single-target detection, seabed classification, and integration with environmental or survey metadata. Approaches are tailored to the dataset, survey design, and project objectives.
Feature detection and ecology
Extraction and analysis of biological features such as fish schools, krill swarms, and scattering layers, with a focus on their structure, behaviour, and environmental context.
This includes characterising feature morphology, scattering properties, vertical distribution, and temporal dynamics, and linking observations to environmental drivers. Outputs support ecological analysis, stock assessment, and interpretation of spatial and temporal patterns in marine and freshwater systems.
Scalable workflows and systems
Development of end-to-end processing pipelines that take acoustic data from raw input through to model outputs and reporting.
This includes automated workflows, efficient processing of large datasets, and integration with external data sources. Systems can be deployed as interactive dashboards, analysis tools, or secure web services (e.g. Azure-based platforms), supporting data access, quality control, and reproducible analysis at scale.
NEKTOFORM – converts acoustic data into fisheries decision support
NEKTOFORM is a private, client-focused platform for turning echosounder data into practical outputs for forecasting, trip planning, search efficiency, target selection, and operational decision-making. It supports expert review of fish schools, swarms, and scattering layers, then combines those observations with environmental data and proprietary models.
Built for real-world acoustic data, NEKTOFORM helps fisheries clients decide where to fish, when to go, which conditions to target, which schools or swarms to prioritise, and how to use vessel time more efficiently. While fisheries are the primary focus, the same expert-led workflow can also support offshore wind, environmental monitoring, impact assessment, and research surveys.
Example projects
A selection of projects demonstrating the processing, analysis, and interpretation of acoustic and ecological datasets across fisheries, marine, and environmental applications.
Automated fish monitoring from imaging sonar data
Development of an end-to-end system for processing ARIS imaging sonar data to estimate fish counts and support monitoring programmes.
Raw sonar data are transformed into track-level datasets and analysed using a multi-stage modelling framework incorporating machine learning, uncertainty estimation, and statistical modelling.
Outputs are generated at 30-minute resolution and aggregated to daily and monthly summaries, with associated uncertainty.
An interactive application supports validation, exploration, and reporting, allowing users to inspect data, assess model behaviour, and generate outputs for downstream use.
The system is operational on multi-year datasets and supports routine monitoring and scalable analysis across sites and conditions.
Have acoustic data that needs processing?
Send a short description of your dataset or project and we will get back to you quickly.
We can review your data, advise on approach, and provide a clear path to usable outputs — whether the focus is acoustic analysis, ecological modelling, or integrated data workflows.