用信息论和AI分析动物叫声，不依赖人工分类
chatter: a Python library for applying information theory and AI/ML models to animal communication
- 将叫声转化为高维隐空间轨迹，避免人为分段
- 支持鸟类、蝙蝠、鲸类等多物种分析
- 适合研究复杂交流系统的生态学家与神经科学家
动物交流研究常需将声音单元分类（如鸣禽的音节、座头鲸的音符），但此方法会忽略真实交流系统的复杂性。chatter 是一款新的 Python 库，通过信息论与现代机器学习技术，在连续隐空间中分析动物交流。该工具不依赖物种分类，已应用于鸟、蝙蝠、鲸类和灵长类的发声数据。它采用变分自编码器、视觉变压器等多种架构，将发声序列表示为高维隐空间中的轨迹，跳过手动或自动的单元分类步骤。提供从预处理、分割到模型训练与特征提取的全流程支持，帮助研究人员量化发声序列的复杂性、可预测性、相似性与新颖性。
原文摘要 · Abstract (English)
The study of animal communication often involves categorizing units into types (e.g. syllables in songbirds, or notes in humpback whales). While this approach is useful in many cases, it necessarily flattens the complexity and nuance present in real communication systems. chatter is a new Python library for analyzing animal communication in continuous latent space using information theory and modern machine learning techniques. It is taxonomically agnostic, and has been tested with the vocalizations of birds, bats, whales, and primates. By leveraging a variety of different architectures, including variational autoencoders and vision transformers, chatter represents vocal sequences as trajectories in high-dimensional latent space, bypassing the need for manual or automatic categorization of units. The library provides an end-to-end workflow -- from preprocessing and segmentation to model training and feature extraction -- that enables researchers to quantify the complexity, predictability, similarity, and novelty of vocal sequences.
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