soundscape_IR: A source separation toolbox for exploring acoustic diversity in soundscapes

1. Soundscapes contain rich acoustic information associated with animal behaviors, environmental characteristics, and human activities, providing opportunities for predicting biodiversity changes and associated drivers. However, assessing the diversity of animal vocalizations remains challenging due to the interference of environmental and anthropogenic noise. A tool for separating sound sources and delineating changes in acoustic signals is crucial for an effective assessment of acoustic diversity.
2. We present soundscape_IR, an open-source Python toolbox dedicated to soundscape information retrieval in which non-negative matrix factorization is applied. This toolbox provides algorithms for supervised and unsupervised source separation (SS). It also enables the use of a snapshot recording for model training and subsequently applying adaptive and semi-supervised SS when target species produce sounds with varying features and when unseen sound sources are encountered.
3. Our results dem...

1. 声景（Soundscapes）蕴含着与动物行为、环境特征及人类活动相关的丰富声学信息，为预测生物多样性变化及其驱动机制提供了可行途径。然而，受环境噪声与人为噪声的干扰，评估动物发声多样性仍颇具挑战。一款能够分离声源并刻画声学信号变化的工具，对于高效评估声学多样性而言至关重要。
2. 我们推出了soundscape_IR——一款专注于声景信息检索（soundscape information retrieval）的开源Python工具箱，其应用了非负矩阵分解（non-negative matrix factorization）技术。该工具箱提供了有监督与无监督声源分离（source separation, SS）算法。此外，当目标物种发出的声音特征存在差异，或遇到未被收录的声源时，可利用快照式录音开展模型训练，随后可应用自适应与半监督声源分离技术。
3. 我们的研究结果显……