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.

翻译：理解社会性动物声音交流的演化是一个重要的研究问题。在此背景下，除了人类之外，分析其他社会性动物（如狐獴、狨猴、猿类）的发声也受到关注。虽然现有方法已针对某些物种的发声进行分析，但尚缺乏一种专为狐獴叫声设计的可靠方法。为此，本文研究了用于狐獴叫声自动分析的特征表示方法。我们探索了基于传统信号处理的表示方法，以及由深度学习进展所推动的数据驱动表示方法。在两个数据集上进行的叫声类型分类研究表明，为人类语音处理开发的特征提取方法可有效应用于狐獴叫声的自动分析。