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About this paper
Aditya Bhattacharjee, Marco Pasini, Emmanouil Benetos
Researchers at Queen Mary University of London developed a Rectified Flow model to generate synthetic audio fingerprint embeddings, enabling scalable evaluation of audio identification systems up to 100 million entries without requiring large, real audio datasets. This approach captures the statistical distribution of real fingerprints, allowing for accurate simulation of retrieval performance degradation under vast search spaces.
Key insights
- Rectified Flow models can effectively capture the statistical characteristics of real audio fingerprint embeddings, achieving high fidelity in generated synthetic samples.
- Synthetic distractors closely replicate the retrieval burden of real distractors, producing performance degradation curves that accurately mirror those obtained with real data.
- Scalability remains a fundamental challenge for current neural audio fingerprinting systems, with performance significantly declining at database sizes reaching millions, and the extent of degradation varies by architectural design.
Summary by alphaXiv.
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