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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2026
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2604.11560 |
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| _version_ | 1866914469404213248 |
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| author | Kather, Vincent S. Haupert, Sylvain Ghani, Burooj Stowell, Dan |
| author_facet | Kather, Vincent S. Haupert, Sylvain Ghani, Burooj Stowell, Dan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_11560 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | bacpipe: a Python package to make bioacoustic deep learning models accessible Kather, Vincent S. Haupert, Sylvain Ghani, Burooj Stowell, Dan Machine Learning Artificial Intelligence 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. |
| title | bacpipe: a Python package to make bioacoustic deep learning models accessible |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2604.11560 |