NOaa
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| Format: | Recurso digital |
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Zenodo
2026
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| _version_ | 1866901995248418816 |
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| author | Roesch, Karl |
| author_facet | Roesch, Karl |
| contents | <p><strong>Acoustic Manifold Transformation</strong> derived from long-form, deep-sea hydrophone audio provided by the NOAA Passive Bioacoustic program. By processing 3-hour audio files into sequential 1-minute waveform segments and passing them through a spatial-geometric filter, the dataset translates raw acoustic pressure fluctuations into a structured 12-dimensional vector space <span class="math-inline">$(t, x, y, z, R, G, B, N_x, N_y, N_z, d, \theta)$</span>. Instead of mapping geographic positions, the spatial coordinates <span class="math-inline">$(x, y)$</span> establish a localized time-versus-displacement grid for each frame, while the vertical axis <span class="math-inline">$(z)$</span> represents instantaneous acoustic energy. Downstream calculated metrics, such as surface normals (<span class="math-inline">$N$</span>) and theta angles (<span class="math-inline">$\theta$</span>), allow the engine to perform real-time pattern recognition—mathematically isolating high-energy, continuous anthropogenic noise (like ship engines) as stable "acoustic ridges" and transient biological signals (like whale vocalizations) as sharp geometric fractures against the ocean's ambient background baseline.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_20245085 |
| institution | Zenodo |
| language | |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | NOaa Roesch, Karl <p><strong>Acoustic Manifold Transformation</strong> derived from long-form, deep-sea hydrophone audio provided by the NOAA Passive Bioacoustic program. By processing 3-hour audio files into sequential 1-minute waveform segments and passing them through a spatial-geometric filter, the dataset translates raw acoustic pressure fluctuations into a structured 12-dimensional vector space <span class="math-inline">$(t, x, y, z, R, G, B, N_x, N_y, N_z, d, \theta)$</span>. Instead of mapping geographic positions, the spatial coordinates <span class="math-inline">$(x, y)$</span> establish a localized time-versus-displacement grid for each frame, while the vertical axis <span class="math-inline">$(z)$</span> represents instantaneous acoustic energy. Downstream calculated metrics, such as surface normals (<span class="math-inline">$N$</span>) and theta angles (<span class="math-inline">$\theta$</span>), allow the engine to perform real-time pattern recognition—mathematically isolating high-energy, continuous anthropogenic noise (like ship engines) as stable "acoustic ridges" and transient biological signals (like whale vocalizations) as sharp geometric fractures against the ocean's ambient background baseline.</p> |
| title | NOaa |
| url | https://doi.org/10.5281/zenodo.20245085 |