Daniel Plastina

Using audio fingerprinting for duplicate detection and thumbnail generation

Audio fingerprinting is a powerful tool for identifying file-based or streaming audio, using a database of fingerprints. The paper presents two new applications of audio fingerprinting: duplicate detection, whose goal is to identify duplicate audio clips in a set, even if they differ in compression quality or duration, and thumbnail generation, which aims to provide a representative short clip of a music track. Neither application requires an external database of fingerprints. Thanks to the robustness of the fingerprinting engine, both applications perform well; the duplicate detector has a false positive rate that is conservatively bounded above by 1% on a very large data set, and the thumbnail generator significantly outperforms using a fixed window.