Mining for scalar representations of emotions in music databases

This dissertation examines an important issue in the Music Information Retrieval domain: codifying the classification of harmonic pitches for the purpose of discovering action rules to interact with scalar music theory. Our intent is twofold: (1) Codify scalar music theory for the purpose of classification rules mining to build a system for automatic indexing of music by scale, region, genre, and emotion, (2) Use action rules mining to permit developers to manipulate a search's resultant composition's genre and tension while still retaining the bulk of original music score. We propose a categorization system for music based upon classification rules and action rules. Herein is a procedure that draws categorization from temporal and spectral attributes of signals that result in a structure conducive for data mining emotions in music based upon classification rules and action rules.

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