Energy classification-assisted fingerprint system for content-based audio copy detection

Abstract

In recent years, fingerprint systems have been applied more and more widely in content-based audio copy detection. However, in the face of harsh conditions, the performance of traditional systems is insufficient. In this paper, we present an improved fingerprint system oriented towards the Content Based Copy Detection (CBCD) task in TRECVID. We extracted the Sign of Energy Band Differences feature for each audio clip and apply energy binary classification for each frame of the clip, and then applied this feature in the matching algorithm. We also refined the index mechanism and added postprocessing to increase speed and eliminate false alarms. The experimental results show that in the worst case scenario, the recall rate reaches 94.40%, with a precision rate of 100%. The results also indicate that the system is robust against several distortions, and can process audio in real time with its index mechanism.