scribe non-hierarchical data and knowledge and to emphasize multiplicity, as in music having various aspects.
This system will handle different
data sets that constitute big data, so it will be composed
of multiple tools and algorithms:
A. Content-Based Information: Mel-scale Frequency Cepstral Coefficients and Matlab.
C. Document-Oriented Data:
a. Semantics: Apache Lucent project (http://lucene.apache.org/core/).
b. Searching: Elastic Search (http://www.elasticsearch.org/).
Languages: JAVA, PHP, and Python.
e. Distributed Computing: Hadoop (http://hadoop.apache.org/).
When adopting melody or rhythm to detect music, we may find that some music has been meshed up with another song. For instance, http://www.youtube.com/watch?v=rN7XP2-bhp0 is an improvisation tool that mixes different songs to produce a new one.
Commercial: Music store search engine.
Academic: Data warehouse and corpus.
a. Structural Analysis of Large Amounts of Music Information (http://ddmal.music.mcgill.ca/salami)
b. Harvesting Speech Datasets for Linguistic Research on the Web (http://www.news.cornell.edu/stories/Jan10/DiggingData.html)
c. Mining a Year of Speech: a Digging into Data challenge (http://www.phon.ox.ac.uk/mining)
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Fields, Benjamin. (2011). Contextualize your listening: The playlist as recommendation engine. Diss. Goldsmiths, University of London, 2011. Print.
Freeman, Linton C. and Alan P. Merriam. (1956). Statistical classification in anthropology: An application to ethnomusicology. American Anthropologist, New Series, 58(3) (June 1956):464-472.
Scaringella, Nicolas, Giorgio Zoia, and Daniel J. Mlynek. (2006). Automatic genre classification of music content: A survey. Signal Processing Magazine, IEEE, 23(2).
Zhao, Zhijun, Lingyun Xie, Jing Liu, and Wen Wu. (2010). The analysis of mood taxonomy comparison between Chinese and Western music. Signal Processing Systems (ICSPS), 2010, 2nd International Conference, 1:606-610.