让生态学家也能轻松用深度学习分析动物叫声
bacpipe: a Python package to make bioacoustic deep learning models accessible
- 提供图形和编程双接口，一键运行前沿声学模型
- 支持自定义音频数据生成声学特征向量与分类结果
- 适合生态、计算机跨学科研究者快速上手
过去几十年，被动声学监测已记录数百万小时自然声音。深度学习模型的进展极大加速了这些数据的分析。尽管新模型不断突破性能上限，但如何有效使用它们仍不直观。为此，我们推出bacpipe——一个集成了生物声学深度学习模型与评估流程的模块化软件包，通过图形界面和编程接口，面向生态学家与计算机科学家。bacpipe可对自定义音频数据集进行处理，生成声学特征向量（embeddings）与分类预测结果，并支持交互式可视化、聚类与探查分析。其模块化设计便于模型评估与基准测试。我们相信，提升深度学习工具对更广泛用户的可及性，将推动生态与进化领域在生物声学中提出新问题并获得答案。
原文摘要 · Abstract (English)
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.
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