Music distribution applications: Challenges

From MIReS

Latest revision as of 23:07, 25 March 2013

• Demonstrate better exploitation possibilities of MIR technologies. The challenge is to convince stakeholders of the value of the technology provided by the MIR community and help them find new revenue streams from their digital assets which are additive and non-cannibalising to existing revenue channels. For these technologies to be relevant they should re-valorise the digital music product, help reduce piracy, streamline industry processes, and reduce inefficiencies.

• Develop systems that go beyond recommendation, towards discovery. Systems have to go beyond simple recommendation and playlisting by supporting discovery and novelty as opposed to predictability and familiarity. This should be one way of making our systems interesting and engaging for prospective users.

• Develop music similarity methods for particular applications and contexts. This means that results produced by computers have to be consistent with human experience of music similarity. Therefore it will be necessary to research methods of personalising our systems to individual users in particular contexts instead of providing one-for-all services.

• Develop systems which cater to the scale of Big Data. The data sets might be songs, users or any other music related elements. From a non-functional perspective, the algorithms and tools themselves should be fast enough to run with sublinear performance on very large datasets so they can easily enable solutions for streaming and subscription services. Beyond raw performance such as processing speed, from a functional view, the algorithms have to be designed to handle the organisation of large music catalogues and the relevance weighting of rapidly increasing quantities of music data mined from crowd-sourced tagging and social networks. Applying algorithms to those big datasets may reveal problems and new research scenarios which were not obvious in the first place.

• Develop large scale robust identification methods for recordings and works. Performing rights organisations and record companies are shifting towards fingerprinting technologies for complete solutions for tracking their affiliates’/partners’ music and for managing their music catalogues. While music fingerprinting has been around for years and it has been widely used, new use cases which require extensive R&D are arising: copyright enforcement for songs and compositions in noisy and live environments and music metadata autotagging among others. Also, finding phylogenetic relationships between songs/performances available on the web, such as "is a remix of" or "is the live version of", may unlock new application scenarios based on music object relationship graphs such as multimodal trust and influence metering in social networks.

• Develop music metadata cleaning techniques. One common feedback from all industry stakeholders such as record companies, music services, music distributor and PROs is the lack of so-called "clean music databases". The absence of clean music databases causes broken links between data from different systems and incorrect editorial metadata tagging for music recordings, which ultimately affects the perceived end-user quality of the applications and services relying on MIR technologies. We encourage the MIR community to address music metadata cleaning by using music analysis and fingerprinting methods as well as text-based techniques borrowed from neighbouring research fields such as text information retrieval and data management among others.

• Develop music detection technology for broadcast audio streams. The media industry is lacking the means for accurately detecting when music (including background music) has been broadcast, in order to transparently handle music royalty payments. This technology should go beyond music vs speech discrimination and address real life use cases such as properly discriminating music vs generic noise.

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