From OPIMA to MPEG IPMP-X: A standard's history across R&D projects

This paper describes the work performed by a number of companies and universities who have been working as a consortium under the umbrella of the European Union Framework Programme 5 (FP5), Information Society Technologies (IST) research program, in order to provide a set of Digital Rights Management (DRM) technologies and architectures, aiming at helping to reduce the copyright circumvention risks, that have been threatening the music and film industries in their transition from the “analogue” to “digital” age. The paper starts by addressing some of the earlier standardization efforts in the DRM arena, namely, Open Platform Initiative for Multimedia Access (OPIMA). One of the described FP5 IST projects, Open Components for Controlled Access to Multimedia Material (OCCAMM), has developed the OPIMA vision. The paper addresses also the Motion Pictures Expert Group—MPEG DRM work, starting from the MPEG Intellectual Propriety Management and Protection—IPMP “Hooks”, towards the MPEG IPMP Extensions, which has originated the first DRM-related standard (MPEG-4 Part 13, called IPMP Extensions or IPMP-X) ever released by ISO up to the present days.2 The paper clarifies how the FP5 IST project MPEG Open Security for Embedded Systems (MOSES), has extended the OPIMA interfaces and architecture to achieve compliance with the MPEG IPMP-X standard, and how it has contributed to the achievement of “consensus” and to the specification, implementation (Reference Software) and validation (Conformance Testing) of the MPEG IPMP-X standard.info:eu-repo/semantics/acceptedVersio

Classification of audio signals using statistical features on time and wavelet transform domains

This paper presents a study on musical signal classification, using wavelet transform analysis in conjunction with statistical pattern recognition techniques. A comparative evaluation between different wavelet analysis architectures in terms of their classification ability, as well as between different classifiers is carried out. We seek to establish which statistical measures clearly distinguish between the three different musical styles of rock, piano, and jazz. Our preliminary results suggest that the features collected by the adaptive splitting wavelet transform technique performed better compared to the other wavelet based techniques, achieving overall classification accuracy of 91.67, using either the Minimum Distance Classifier or the Least Squares Minimum Distance Classifier. Such a system can play a useful part in multimedia applications which require content based search, classification, and retrieval of audio signals, as defined in MPEG-7

The Challenge: From MPEG Intellectual Property Rights Ontologies to Smart Contracts and Blockchains

The Moving Picture Experts Group (MPEG) is an International Organization for Standardization/International Electrotechnical Commission (ISO/IEC) working group that develops media coding standards. These standards include a set of ontologies for the codification of intellectual property rights (IPR) information related to media. The Media Value Chain Ontology (MVCO) facilitates rights tracking for fair, timely, and transparent payment of royalties by capturing user roles and their permissible actions on a particular IP entity. The Audio Value Chain Ontology (AVCO) extends MVCO functionality related to the description of IP entities in the audio domain, e.g., multitrack audio and time segments. The Media Contract Ontology (MCO) facilitates the conversion of narrative contracts to digital ones. Furthermore, the axioms in these ontologies can drive the execution of rights-related workflows in controlled environments, e.g., blockchains, where transparency and interoperability is favored toward fair trade of music and media. Thus, the aim of this article is to create awareness of the MPEG IPR ontologies developed in the last few years and the work currently taking place addressing the challenge identified toward the execution of such ontologies as smart contracts on blockchain environments

Classification of audio signals using statistical features on time and wavelet transform domains

This paper presents a study on musical signal classification, using wavelet transform analysis in conjunction with statistical pattern recognition techniques. A comparative evaluation between different wavelet analysis architectures in terms of their classification ability, as well as between different classifiers is carried out. We seek to establish which statistical measures clearly distinguish between the three different musical styles of rock, piano, and jazz. Our preliminary results suggest that the features collected by the adaptive splitting wavelet transform technique performed better compared to the other wavelet based techniques, achieving overall classification accuracy of 91.67, using either the Minimum Distance Classifier or the Least Squares Minimum Distance Classifier. Such a system can play a useful part in multimedia applications which require content based search, classification, and retrieval of audio signals, as defined in MPEG-7.</p