Waveprint: Efficient wavelet-based audio fingerprinting

摘要

In this paper, we present Waveprint, a novel method for audio identification. Waveprint uses a combination of computer-vision techniques and large-scale data-stream processing algorithms to create compact fingerprints of audio data that can be efficiently matched. The resulting system has excellent identification capabilities for small snippets of audio that have been degraded in a variety of manners, including competing noise, poor recording quality and cell-phone playback. We explicitly measure the tradeoffs between performance, memory usage, and computation through extensive experimentation. The system is more efficient in terms of memory usage and computation, while being more accurate when compared with previous state of the art systems. The applications of Waveprint include song identification for end-consumer use, copyright protection for audio assets, copyright protection for television assets and synchronization of off-line audio sources, such as live television.