%Aigaion2 BibTeX export from Idiap Publications
%Sunday 17 November 2024 08:22:07 AM

@INPROCEEDINGS{BenMahmoud_VIHAR_2024,
         author = {Ben Mahmoud, Imen and Sarkar, Eklavya and Manser, Marta and Magimai.-Doss, Mathew},
       keywords = {bioacoustics, call type classification, feature representations, self-supervised learning},
       projects = {Idiap, EVOLANG},
          title = {Feature Representations for Automatic Meerkat Vocalization Classification},
      booktitle = {4th International Workshop on Vocal Interactivity in-and-between Humans, Animals and Robots},
           year = {2024},
       crossref = {BenMahmoud_Idiap-RR-06-2024},
       abstract = {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 of other social animals such as, meerkats, marmosets, apes. While existing approaches address vocalizations of certain species, a reliable method tailored for meerkat calls is lacking. To that
extent, this paper investigates feature representations for automatic meerkat vocalization analysis. Both traditional signal processing-based representations and data-driven representations facilitated by advances in deep learning are explored.
Call type classification studies conducted on two data sets reveal that feature extraction methods developed for human speech processing can be effectively employed for automatic meerkat call analysis.},
            pdf = {https://publications.idiap.ch/attachments/papers/2024/BenMahmoud_VIHAR_2024.pdf}
}



crossreferenced publications: 
@TECHREPORT{BenMahmoud_Idiap-RR-06-2024,
         author = {Ben Mahmoud, Imen and Sarkar, Eklavya and Manser, Marta and Magimai.-Doss, Mathew},
       projects = {Idiap},
          month = {8},
          title = {Feature Representations for Automatic Meerkat Vocalization Classification},
           type = {Idiap-RR},
         number = {Idiap-RR-06-2024},
           year = {2024},
    institution = {Idiap},
       abstract = {Understanding the evolution of vocal communication in social animals is an important research problem. In that context, beyond humans, there is an interest in analyzing vocalizations of other social animals such as, meerkats, marmosets, apes. While existing approaches address vocalizations of certain species, a reliable method tailored for meerkat calls is lacking. To that extent, this paper investigates feature representations for automatic meerkat vocalization analysis. Both traditional signal processing-based representations and data-driven representations facilitated by advances in deep learning are explored. Call type classification studies conducted on two data sets reveal that feature extraction methods developed for human speech processing can be effectively employed for automatic meerkat call analysis.},
            pdf = {https://publications.idiap.ch/attachments/reports/2024/BenMahmoud_Idiap-RR-06-2024.pdf}
}