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Smooth functions can represent dolphin signature whistle frequencies. Here, we consider each whistle evaluated at the same number of equally spaced time-points. These frequencies and the original sound-lengths are modeled by a mixture model based on the Dirichlet process estimated by MCMC. The results are promising and show the ability of the model to differentiate between different shapes of the sounds.

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Clustering Dolphin Signature Whistles with Dirichlet Process Mixtures

  • 会议论文

  • 发表日期：

    2025年

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  • Gianluca Mastrantonio,
  • Giovanna Jona Lasinio,
  • Petra Oswine Pammer,
  • Giulia Pedrazzi,
  • Daniela Silvia Pace,
  • Maria Silvia Labriola

摘要

Smooth functions can represent dolphin signature whistle frequencies. Here, we consider each whistle evaluated at the same number of equally spaced time-points. These frequencies and the original sound-lengths are modeled by a mixture model based on the Dirichlet process estimated by MCMC. The results are promising and show the ability of the model to differentiate between different shapes of the sounds.