Novel software for deep-learning based acoustic data analysis

Abstract

In recent years, deep neural networks have been successfully applied to solve a range of detection and classification tasks in underwater acoustics, outperforming existing methods. However, deep learning models are “data hungry” requiring large amounts of accurately labelled acoustic samples to train. Moreover, a certain amount of “fine tuning” is often required to achieve satisfactory performance in a new acoustic environment. Thus, the development of deep learning models depends on the input of expert human analysts, both for building the initial training set and for adjusting the model's performance. However, open-source software to facilitate this collaboration between machine learning developers and acousticians is currently lacking. To address this need, our team is building a web-based application for collaboratively annotating sound samples and validating model predictions. The user interface is designed to be familiar to acousticians, while machine learning developers have access to a dashboard allowing them to efficiently leverage the acousticians' expert knowledge. In this contribution, an overview of the application will be given and its functionalities will bedemonstrated through its application to the HALLO (Humans and ALgorithms Listening for Orcas) project. Future developments will also be described, highlighting complementary applications under development such as a model adaptation tool.