3 citations · 4 across the 5 of their papers we have counts for
5 papers
Towards Leveraging Sequential Structure in Animal Vocalizations
Eklavya Sarkar, Mathew Magimai. -Doss
Animal vocalizations contain sequential structures that carry important communicative information, yet most computational bioacoustics studies average the extracted frame-level fea…
Comparing Self-Supervised Learning Models Pre-Trained on Human Speech and Animal Vocalizations for Bioacoustics Processing
Eklavya Sarkar, Mathew Magimai. -Doss
Self-supervised learning (SSL) foundation models have emerged as powerful, domain-agnostic, general-purpose feature extractors applicable to a wide range of tasks. Such models pre-…
Feature Representations for Automatic Meerkat Vocalization Classification
Imen Ben Mahmoud, Eklavya Sarkar, Marta Manser +1
Understanding evolution of vocal communication in social animals is an important research problem. In that context, beyond humans, there is an interest in analyzing vocalizations o…
On the Utility of Speech and Audio Foundation Models for Marmoset Call Analysis
Eklavya Sarkar, Mathew Magimai. -Doss
Marmoset monkeys encode vital information in their calls and serve as a surrogate model for neuro-biologists to understand the evolutionary origins of human vocal communication. Tr…
Predicting Heart Activity from Speech using Data-driven and Knowledge-based features
Gasser Elbanna, Zohreh Mostaani, Mathew Magimai. -Doss
Accurately predicting heart activity and other biological signals is crucial for diagnosis and monitoring. Given that speech is an outcome of multiple physiological systems, a sign…