Underwater Noise Identification

Underwater noise identification from acoustic recordings using spectrograms


To develop, using the iMagine platform, a prototype service for processing acoustic underwater recordings for identification and recognition of marine species and other noise types (e.g., offshore piling).

Development actions during iMagine


Setting up development environment at iMagine platform


Connecting the Mongo database to the iMagine platform for ingesting and extracting data


Developing, training and refining the AI model at the iMagine platform


Enhancing the FAIRness of data output in accordance with community standards and relevant vocabularies


Documenting approach and resulting prototype


Contributing to dissemination and outreach

Objective and challenge

Underwater sound is crucial for aquatic life and survival. It consists of various sources, including living organisms, natural phenomena, and human-made noises. The European Union’s Marine Strategy Framework Directive (MSFD) recognizes underwater noise as a pollutant. In this context, the iMagine platform is being utilized to create a prototype service for analyzing acoustic underwater recordings. The goal is to identify and recognize marine species as well as other sound types, such as shipping noises.

Currently, there is a collection of 1.5 years’ worth of underwater sound data, with ongoing data collection efforts. However, the process of processing this data and identifying the sources is time-consuming, requiring significant individual effort. Furthermore, the existing process lacks automation. To address these challenges, the use case will leverage the iMagine AI platform and the project’s expertise to enhance data labeling and explore different AI techniques for sound recognition and identification.

Development timeline

To develop the solution, the use case begins with importing raw sound data into their database (MongoDB). They enhance the labeling and validation interface to streamline the process of labeling the data, ensuring greater efficiency in preparing the training dataset. Once they have a sufficient amount of labeled data, they proceed to develop, train, and validate multiple AI models. Once they identify a high-performing AI model, they focus on automating the sound identification process.

Expected Results


The underwater sound classification service has large potential to support the scientific community in marine biodiversity and ecosystem research. Underwater sound and noise pollution are increasingly recognized as an essential indicator of healthy seas and oceans. 

Detecting and classifying different species can contribute to species abundance assessment, and detection and characterization of noise pollution can provide insight in effects of human activities on marine life.


With this, the scientific interest in underwater sound detection and classification is rapidly increasing. Potential policy supporting examples are downstream applications in monitoring for MSFD (as part of the 11th GES) and OSPAR Underwater noise indicators.

Involved Partners