Abstract
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 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.
Highlights
This paper describes the details of a novel framework for robust perceptual hashing of audio content
The proposed framework for robust perceptual hashing consists of two sub-systems: The first system generates of extracts the hash values from the audio content, while the second sub-system applies an efficient search scheme to identify the extracted hash value from an existing multimedia database that represents the stored content by their extracted hash values
Instead of using the audio content in the search/identification/retrieval operations, we will base these operations on the extracted hash values which allow for efficient database queries
Summary
Abstract— 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. These collections are produced/consumed by different groups of users such as the entertainment, music, movie and animation industries. We propose a novel algorithm for robust perceptual hashing of musical content using balanced multiwavelets (BMW). The generated hash values are used for identifying, searching, and retrieving musical content from large musical databases. 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
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
