Data processing methodologies: Challenges

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<onlyinclude> * '''Systematise cross-disciplinary transfer of methodologies.''' Early breakthroughs in MIR came from a relatively limited number of external fields, mainly through the contributions of individual researchers working in neighbouring fields e.g. Speech Processing and applying their methodologies to music. Being more systematic about this implies two challenges for the MIR community: first, to stay up-to-date with latest developments in disciplines that were influential in some points of MIR evolution, and second to define ways to ''systematically'' identify potentially relevant methodologies from neighbouring disciplines. * '''Take advantage of the multiple modalities of music data.''' Music exists in many diverse modalities (audio, text, video, score, etc.) which in turn call for different processing methodologies. Given a particular modality of interest e.g. audio, in addition to identifying promising processing methodologies from neighbouring fields dealing with the same modality e.g. speech processing, an effort will have to be made to apply methodologies ''across'' modalities. Further, as music exists simultaneously in diverse modalities, another challenge for MIR will be to include methodologies from cross-modal processing, i.e. using ''joint'' representations/models for data that exists, and can be represented, simultaneously in diverse modalities. * '''Adopt recent Machine Learning techniques.''' As exemplified above, MIR makes a great use of machine learning methodologies, in particular many tasks are formulated according to a batch learning approach where a fixed amount of annotated training data is used to learn models which can then be evaluated with similar data. However, music data can now be found in very large amounts (e.g. in the scale of hundreds of thousands of items for music pieces in diverse modalities, or in the scale of tens of millions in the case of e.g. tags), music is increasingly existing in data streams rather than in data ''sets'', and the characterisation of music data can evolve with time (e.g. tag annotations are constantly evolving, sometimes even in an adverse way). These data characteristics (i.e. very large amounts, streaming, non-stationarity) Big Data characteristics imply a number of challenges for MIR, such as data acquisition, dealing with weakly structured data formats, scalability, online (and real-time) learning, semi-supervised learning, iterative learning and model updates, learning from sparse data, learning with only positive examples, learning with uncertainty, etc. (see e.g. Yahoo! Labs “key scientific challenges” in [http://labs.yahoo.com/ksc/Machine_Learning Machine Learning] and the White Paper [http://cra.org/ccc/docs/init/bigdatawhitepaper.pdf “Challenges and Opportunities with Big Data”] published by the Computing Community Consortium). <!-- * So far, inspiration came from a relatively limited number of external fields, mainly through of the works of individuals (e.g. Logan and Ellis for Speech Processing methodologies). One challenge for the MIR community is to more systematically identify potentially relevant methodologies from other disciplines. * In a very multi-disciplinary field, such as MIR, it may be a challenge to stay up-to-date with latest developments in disciplines that were influential in some points of MIR evolution. In other words, how can MIR stay up-to-date with research in Text IR, Machine Learning, Speech processing, etc.? * Multimodality will be a key challenge in upcoming years. * Another challenge with be to include methodologies from cross-modality processing, i.e. using joint representation models for data that may exist in diverse modalities (e.g. text, audio, image, video) * Data mining methodologies will have to be adapted for dealing with very large amounts of data. The MIR community will likely have to dedicate more efforts to dealing with methodologies of “constant” learning (i.e. active and semi-supervised learning), in opposition to offline learning to which it is more used to. * Relevant methodological challenges also include challenges related to evaluation (see [[Evaluation of research results]]). --> </onlyinclude> <!-- <comments /> {{:Talk:{{PAGENAME}}}}__NOEDITSECTION__ --> [[Data processing methodologies|Back to previous page]]

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