This session was presented in the conference: Human-Animal Relations - Challenges and Opportunities in Changing Realities, which took place virtually on June 1-3, 2021.
Shane Gero, Carleton University; Founder of The Dominica Sperm Whale Project and
Michael Bronstein, Imperial College London; Twitter, UK; Project CETI, Dominica
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. We present a roadmap toward this
goal based on currently existing technology and 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.
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