Feature Representations for Automatic Meerkat Vocalization Classification
This work addresses the need for a tailored method for meerkat vocalization analysis, which is incremental as it adapts existing techniques to a new domain.
The paper tackled the problem of automatically classifying meerkat vocalizations by exploring feature representations, finding that methods developed for human speech processing can be effectively used, achieving reliable classification on two datasets.
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.