ProtoSSL discovers generalizable prototypes from unlabeled time-series via self-supervision and assigns them to new tasks for interpretable predictions, outperforming supervised baselines in low-data regimes on ECG datasets.
AudioProtoPNet: An interpretable deep learning model for bird sound classification.Ecological Informatics, 87: 103081
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Bacpipe is a modular Python package that integrates state-of-the-art bioacoustic deep learning models to generate embeddings, classifier predictions, and evaluation pipelines for custom audio data.
The paper introduces CoarseSoundNet, a deep learning model for classifying biophony, geophony, and anthropophony in passive acoustic monitoring recordings, reporting performance gains from additional similar data, a silence class, and decision thresholds, plus a case study on acoustic index trends.
citing papers explorer
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ProtoSSL: Interpretable Prototype Learning from Unlabeled Time-Series Data
ProtoSSL discovers generalizable prototypes from unlabeled time-series via self-supervision and assigns them to new tasks for interpretable predictions, outperforming supervised baselines in low-data regimes on ECG datasets.
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bacpipe: a Python package to make bioacoustic deep learning models accessible
Bacpipe is a modular Python package that integrates state-of-the-art bioacoustic deep learning models to generate embeddings, classifier predictions, and evaluation pipelines for custom audio data.
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CoarseSoundNet: Building a reliable model for ecological soundscape analysis
The paper introduces CoarseSoundNet, a deep learning model for classifying biophony, geophony, and anthropophony in passive acoustic monitoring recordings, reporting performance gains from additional similar data, a silence class, and decision thresholds, plus a case study on acoustic index trends.