Audio Fingerprinting: Combining Computer Vision & Data Stream Processing

In this paper, we present waveprint, a novel system 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 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.