Abstract
Digital multimedia content (especially audio) is becoming a major part of the average computer user experience. Large digital audio collections of music, audio and sound effects are also used by the entertainment, music, movie and animation industries. Therefore, the need for identification and management of audio content grows proportionally to the increasing widespread availability of such media virtually "any time and any where" over the Internet. In this paper, we propose a novel framework for robust perceptual hashing of audio content using balanced multiwavelets (BMW). The framework for generating robust perceptual hash values (or fingerprints) is described. The generated hash values are used for identifying, searching, and retrieving audio content from large audio databases. Furthermore, we illustrate, through extensive computer simulation, the robustness of the proposed framework to efficiently represent audio content and withstand several signal processing attacks and manipulations.