Dirichlet process mixture model based on topologically augmented signal representation for clustering infant vocalizations
<|reference_start|>Dirichlet process mixture model based on topologically augmented signal representation for clustering infant vocalizations: Based on audio recordings made once a month during the first 12 months of a child's life, we propose a new method for clustering this set of vocalizations. We use a topologically augmented representation of the vocalizations, employing two persistence diagrams for each vocalization: one computed on the surface of its spectrogram and one on the Takens' embeddings of the vocalization. A synthetic persistent variable is derived for each diagram and added to the MFCCs (Mel-frequency cepstral coefficients). Using this representation, we fit a non-parametric Bayesian mixture model with a Dirichlet process prior to model the number of components. This procedure leads to a novel data-driven categorization of vocal productions. Our findings reveal the presence of 8 clusters of vocalizations, allowing us to compare their temporal distribution and acoustic profiles in the first 12 months of life.<|reference_end|>
@article{bonafos2024dirichlet,
    title={Dirichlet process mixture model based on topologically augmented signal
  representation for clustering infant vocalizations},
    author={Guillem Bonafos and Clara Bourot and Pierre Pudlo and Jean-Marc
  Freyermuth and Laurence Reboul and Samuel Tronc{c}on and Arnaud Rey},
    journal={arXiv preprint arXiv:2407.05760},
    year={2024},
    doi={10.21437/Interspeech.2024-394},
    archivePrefix={arXiv},
    eprint={2407.05760},
    primaryClass={stat.AP cs.SD eess.AS stat.ML}
}