Back

Elephant rumble vocalizations: spectral substructures and superstructures

Rantsiou, E.

2024-03-12 animal behavior and cognition
10.1101/2024.03.10.584305 bioRxiv
Show abstract

Elephant communication, particularly through infrasound rumbles, plays a pivotal role in their social interactions, yet the complexity and functional significance of these vocalizations remain only partially understood. This study explores the spectral characteristics of male African Savannah elephant rumbles, revealing a rich substructure within what has traditionally been viewed as homogeneous low-frequency calls. Our formant frequency analysis of wild male elephant rumble vocalizations demonstrates that rumbles exhibit significant within-call spectral variation, challenging the notion of rumbles as uniform acoustic units. Our findings further reveal that elephant rumbles contain complex patterns of frequency modulation, including distinct vowel-like elements, suggestive of sophisticated vocal tract manipulation. We also document the presence of collectively oscillatory behavior in the formant frequencies of elephant rumbles during group vocalization events. This superstructure emerges clearly when silent intervals between rumbles are removed, revealing an oscillatory trend in the chronological sequence of vocalizations. The discovery of intricate spectral substructures and superstructures within elephant rumbles and rumble exchanges respectively, highlights the sophistication of elephant communication systems. This study underlines the elephants ability to engage in complex vocal modulation and may have implications for the understanding of their social organization and cognition. Furthermore, unraveling the complexity of elephant vocalizations can enhance our ability to monitor and conserve these majestic creatures, offering new perspectives for studying their social interactions and behaviors in the wild.

Matching journals

The top 3 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.