Cetacean Translation Initiative: a roadmap to deciphering the communication of sperm whales

The past decade has witnessed a groundbreaking rise of machine learning for human language analysis, with current methods capable of automatically accurately recovering various aspects of syntax and semantics – including sentence structure and grounded word meaning – from large data collections. Recent research showed the promise of such tools for analyzing acoustic communication in nonhuman species. We posit that machine learning will be the cornerstone of future collection, processing, and analysis of multimodal streams of data in animal communication studies, including bioacoustic, behavioral, biological, and environmental data. Cetaceans are unique non-human model species as they possess sophisticated acoustic communications, but utilize a very different encoding system that evolved in an aquatic rather than terrestrial medium. Sperm whales, in particular, with their highly-developed neuroanatomical features, cognitive abilities, social structures, and discrete click-based encoding make for an excellent starting point for advanced machine learning tools that can be applied to other animals in the future. This paper details a roadmap toward this goal based on currently existing technology and multidisciplinary scientific community effort. We outline the key elements required for the collection and processing of massive bioacoustic data of sperm whales, detecting their basic communication units and language-like higher-level structures, and validating these models through interactive playback experiments. The technological capabilities developed by such an undertaking are likely to yield cross-applications and advancements in broader communities investigating non-human communication and animal behavioral research.

https://arxiv.org/abs/2104.08614

最近的机器学习算法可以精确地重构句法和语义,这包括从大规模数据集中提取的句子结构和词汇含义。最近的研究也表明这样的技术可以用于分析动物之间的语言交流。我们使用机器学习的算法分析抹香鲸的交流,抹香鲸拥有高度发达的神经系统,感知能力以及社交结构。这将为未来在其他生物上的研究带来参考。本文详细地展示了一个路线图,这个路线图描绘了如何收集和处理抹香鲸的生物声学信号,侦测基本的沟通单元以及语言相关的高级结构,并且在新的数据上进行验证。

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