bacpipe: a Python package to make bioacoustic deep learning models accessible

2026-04-13

AI Summary

This work proposes bacpipe, a modular Python package designed to bridge the gap between ecologists and computer scientists in bioacoustic research. Despite rapid advances in deep learning, its application to large-scale passive acoustic monitoring data remains hindered by high technical barriers and fragmented tooling. bacpipe addresses these challenges by providing a unified platform that integrates multiple state-of-the-art deep learning models for audio embedding extraction, classification, interactive visualization, and clustering exploration. Built with a plug-and-play architecture, the framework enables straightforward model invocation, evaluation, and comparison, substantially lowering the entry barrier for non-specialists. By streamlining access to advanced analytical capabilities, bacpipe empowers interdisciplinary researchers to efficiently conduct ecological and evolutionary analyses on acoustic datasets.

Technology Category

• Machine Learning: Bio-inspired Learning
• Natural Language Processing: Language Grounding & Multi-modal NLP
• Search and Optimization: Evolutionary Computation

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

1. Natural sounds have been recorded for millions of hours over the previous decades using passive acoustic monitoring. Improvements in deep learning models have vastly accelerated the analysis of large portions of this data. While new models advance the state-of-the-art, accessing them using tools to harness their full potential is not always straightforward. Here we present bacpipe, a collection of bioacoustic deep learning models and evaluation pipelines accessible through a graphical and programming interface, designed for both ecologists and computer scientists. Bacpipe is a modular software package intended as a point of convergence for bioacoustic models. 2. Bacpipe streamlines the usage of state-of-the-art models on custom audio datasets, generating acoustic feature vectors (embeddings) and classifier predictions. A modular design allows evaluation and benchmarking of models through interactive visualizations, clustering and probing. 3. We believe that access to new deep learning models is important. By designing bacpipe to target a wide audience, researchers will be enabled to answer new ecological and evolutionary questions in bioacoustics. 4. In conclusion, we believe accessibility to developments in deep learning to a wider audience benefits the ecological questions we are trying to answer.

Problem

• bioacoustics
• deep learning
• model accessibility
• passive acoustic monitoring
• ecological research

Innovation

• bioacoustics
• deep learning
• model accessibility
• audio embeddings
• modular pipeline