Underwater Noise Identification

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.

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.