Data processing methodologies: Challenges
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* '''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). | * '''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). | ||
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* 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. | * 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. | ||
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[[Data processing methodologies|Back to previous page]] | [[Data processing methodologies|Back to previous page]] | ||
Latest revision as of 22:55, 25 March 2013
- 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 Machine Learning and the White Paper “Challenges and Opportunities with Big Data” published by the Computing Community Consortium).
