Thierry Legou | Searcharxiv

arXiv2024

Assessing the Circadian Rhythm of Cats Living in a Group using Accelerometers

This study explores the biological rhythms of domestic cats. Twelve cats from the AVA shelter in Cuy-Saint-Fiacre, France, participated in the experimental study, wearing collars equipped with IMU sensors for about three weeks. Recorded data were analyzed to measure the cats activity and to gain further insights into their biological rhythms. We first determined the time budget of the cats by categorizing behavior into inactivity and activity. Next, we analyzed the day/night activity repartition and the hourly distribution of activity. Results showed an average of 14.5% of global activity and a higher activity during the day in comparison with the night. Moreover, a bimodal activity pattern with increased activity at the time of the caretaker's interventions at feeding time was found.

eess.SP↗

arXiv2023

Detection and classification of vocal productions in large scale audio recordings

We propose an automatic data processing pipeline to extract vocal productions from large-scale natural audio recordings and classify these vocal productions. The pipeline is based on a deep neural network and adresses both issues simultaneously. Though a series of computationel steps (windowing, creation of a noise class, data augmentation, re-sampling, transfer learning, Bayesian optimisation), it automatically trains a neural network without requiring a large sample of labeled data and important computing resources. Our end-to-end methodology can handle noisy recordings made under different recording conditions. We test it on two different natural audio data sets, one from a group of Guinea baboons recorded from a primate research center and one from human babies recorded at home. The pipeline trains a model on 72 and 77 minutes of labeled audio recordings, with an accuracy of 94.58% and 99.76%. It is then used to process 443 and 174 hours of natural continuous recordings and it creates two new databases of 38.8 and 35.2 hours, respectively. We discuss the strengths and limitations of this approach that can be applied to any massive audio recording.

cs.SD↗