Driven by the recent advances in digital entertainment technologies, digital multimedia content (such as music and movies) is becoming a major part of the average computer user experience. Through daily interaction with digital multimedia content, large digital collections of music, audio and sound effects have emerged. Furthermore, these collections are produced/consumed by different groups of users such as the entertainment, music, movie and animation industries. Therefore, the need for identification and management of such 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 algorithm for robust perceptual hashing of musical content using balanced multiwavelets (BMW). The procedure for generating robust perceptual hash values (or fingerprints) is described in details. The generated hash values are used for identifying, searching, and retrieving musical content from large musical 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.