Article
| Title: | Shape analysis and comparison of audio patterns using divergence measures (English) |
| Author: | Vishwakarma, Amit |
| Author: | Subrahamanian Moosath, K. S. |
| Language: | English |
| Journal: | Applications of Mathematics |
| ISSN: | 0862-7940 (print) |
| ISSN: | 1572-9109 (online) |
| Volume: | 70 |
| Issue: | 4 |
| Year: | 2025 |
| Pages: | 473-493 |
| Summary lang: | English |
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| Category: | math |
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| Summary: | We represent the point clouds of objects and audio signals as manifolds of Gaussian Mixture Models, and analyze the shape variation and compare the audio patterns using three divergence measures, namely the Kullback-Leibler Divergence, Jensen-Shannon Divergence, and Modified Symmetric Kullback-Leibler Divergence. Experiments are conducted on basic geometric shapes, 3D human body shapes, animal shapes, point clouds of the same object produced from the dense point clouds in the PU-GAN (Point Cloud Upsampling Adversarial Network) dataset. Then, we present a method to generate a point cloud of an audio signal using the Short-Time Fourier Transform. The audio-derived point clouds represent frequency, time, and magnitude relationships, enabling analysis of speech and audio patterns. The results across all datasets show that the Modified Symmetric Kullback-Leibler Divergence provides the most distinct and stable comparison between different point clouds, demonstrating its robustness for point cloud comparison. (English) |
| Keyword: | information geometry |
| Keyword: | point cloud |
| Keyword: | Gaussian mixture model |
| Keyword: | statistical manifold |
| Keyword: | divergence |
| Keyword: | computational geometry |
| MSC: | 53B12 |
| MSC: | 62B11 |
| DOI: | 10.21136/AM.2025.0093-25 |
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| Date available: | 2025-10-03T10:24:31Z |
| Last updated: | 2025-10-06 |
| Stable URL: | http://hdl.handle.net/10338.dmlcz/153089 |
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