تقرير
Dirichlet process mixture model based on topologically augmented signal representation for clustering infant vocalizations
| العنوان: | Dirichlet process mixture model based on topologically augmented signal representation for clustering infant vocalizations |
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| المؤلفون: | Bonafos, Guillem, Bourot, Clara, Pudlo, Pierre, Freyermuth, Jean-Marc, Reboul, Laurence, Tronçon, Samuel, Rey, Arnaud |
| سنة النشر: | 2024 |
| المجموعة: | Computer Science Statistics |
| مصطلحات موضوعية: | Statistics - Applications, Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing, Statistics - Machine Learning |
| الوصف: | 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. |
| نوع الوثيقة: | Working Paper |
| DOI: | 10.21437/Interspeech.2024-394 |
| URL الوصول: | http://arxiv.org/abs/2407.05760 |
| رقم الأكسشن: | edsarx.2407.05760 |
| قاعدة البيانات: | arXiv |
| DOI: | 10.21437/Interspeech.2024-394 |
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