POLARIS: Training-Free Audio Fingerprinting with Saliency-Based Landmarks and Delaunay Grouping
Read the original on arXiv Computer Vision →The Flow has not summarised this story yet — read it at arXiv Computer Vision.
The Flow has not summarised this story yet — read it at arXiv Computer Vision.
Variable-Length Audio Fingerprinting (VLAFP) is a new deep learning model that converts audio into low-dimensional fingerprints while allowing variable-length input, unlike existing methods that require fixed-length segments. By supporting variable-length audio during both training and testing, VLAFP captures temporal dynamics that were previously neglected. Experiments demonstrate that VLAFP outperforms current state‑of‑the‑art systems in live audio identification and audio retrieval across three real‑world datasets.
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