hao et al. (2010) have explored how to detect emotions in Chinese and Western music based on timbre, rhythm, and pitch. They used four mood categories (anxiousness, depression, contentment, and exuberance) to classify the music in their auto-detection system. Although their mood classification system is simplified, traditional structured classification cannot be used to integrate their research into the existing system.
Data from ethnomusicology has exposed shortcomings in existing music taxonomy. Linton C. Freeman and Alan P. Merriam (1956) considered gthe individuality of esthetic expression as it is shaped by the customs of a particular group is more sharply established.h They computed frequencies of use of major seconds, minor thirds, and total intervals in 20 Trinidad Rada and Brazilian Ketu songs. After weighting various measurements via a lambda score, the statics displayed the differences between songs in these two areas. They then attempted to solve the classification challenge through statistics rather than distinguishing songs via classification terms. Their approach not only reflected the debater of classification in cultural anthropology but also served as a reminder that creating a hierarchical classification system may be useful only in a specific field, such as the commercial music market.
In contrast, document-oriented data contains more variations. For example, Samingadfs (IϊN) Wandering (¬QL) is a Mandarin song with a Taiwan aboriginal rhythm and melody. In a document-oriented database, this song could be stored in the following categories:
Entity 1. Samingad-> Singer, Wandering, Mandarin, Puyuma (Ϊμ), Taiwan Aboriginal, Nostalgia.
Entity 2. Wandering-> Song, Mandarin, Samingad, Puyuma, Nostalgia.
Entity 3. Nostalgia-> Samingad, Song, Puyuma, Mandarin, Alone.
Entity 4. Lonesome-> Wandering, Mandarin, Nostalgia.
From the examples above, it can be seen that document-oriented taxonomy is flexible and it is similar to Gilles Deleuze and Félix Guattarifs rhizome concept. They adopted this term to describe 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)
Deleuze, Gilles and Félix Guattari. (1980). A Thousand Plateaus. Brian Massumi (trans.). London and New York: Continuum.
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, 1:60