aiqbench analysis
This study applies frozen audio encoders—a technique rooted in deep learning—to analyze acoustic sequence dependence across diverse biological vocalizations, including sperm whales and humans. By standardizing event counts and temporal scales, the research reveals that repertoire differences stem from specific acoustic properties rather than a universal hierarchy, offering new insights into how machine learning models can interpret non-human communication patterns.
Structure Across Voices: Comparing acoustic-event type accumulation and sequence dependence across four vocal repertoires using frozen audio encoders
arXiv:2609.16612v1 Announce Type: cross Abstract: Vocal repertoires can differ in acoustic-event type accumulation and temporal organization, yet direct comparison is difficult because corpora use different native events and unequal amounts of sequence. We compare sperm whale codas, human speech phones, Bengalese finch syllables, and common marmoset calls using the same frozen-audio-encoder procedure while matching event count and local sequence opportunity. Whale shows the fastest type accumulation; Finch shows the strongest immediate dependence and repeated-subsequence recurrence. Physically interpretable acoustics recover complementary parts of this profile, continuous analyses without clustering support broad Whale acoustic coverage, and source- and position-preserving nulls retain both Finch order effects. Extending predictive context shifts the comparison toward Whale. Thus repertoire differences depend on the acoustic property and temporal scale measured rather than forming a single hierarchy.
Abstract:Vocal repertoires can differ in acoustic-event type accumulation and temporal organization, yet direct comparison is difficult because corpora use different native events and unequal amounts of sequence. We compare sperm whale codas, human speech phones, Bengalese finch syllables, and common marmoset calls using the same frozen-audio-encoder procedure while matching event count and local sequence opportunity. Whale shows the fastest type accumulation; Finch shows the strongest immediate dependence and repeated-subsequence recurrence. Physically interpretable acoustics recover complementary parts of this profile, continuous analyses without clustering support broad Whale acoustic coverage, and source- and position-preserving nulls retain both Finch order effects. Extending predictive context shifts the comparison toward Whale. Thus repertoire differences depend on the acoustic property and temporal scale measured rather than forming a single hierarchy.
12 pages, 4 figures. Preprint
Sound (cs.SD); Artificial Intelligence (cs.AI)
arXiv:2609.16612 [cs.SD]
(or arXiv:2609.16612v1 [cs.SD] for this version)
https://doi.org/10.48550/arXiv.2609.16612 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Full text under the publisher's open licence · 225 words
Related coverage

Runway’s WorldPrompt and the Engineering of Real-Time Worlds
Latent Space · Sep 25, 2026

Democratizing institutional knowledge: Building an AI-powered knowledge management system with AWS
AWS · Aug 24, 2026

Generate Autonomous Business Insights with AI Agent and MCP Servers
AWS · Jul 29, 2026
Models Can't Remember Their Training. Neither Can You.
LessWrong · Jul 19, 2026