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Music Genre Classification Alex Stabile. Example File. http://www.ccarh.org/courses/253/files/midifiles-20080227-2up.pdf. Organization/Parsing file. Beat class Notes on beat Notes off beat Beat number (8). Chord Identification. Notes: C, E, G What kind of chord? Look at intervals…
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Music Genre Classification Alex Stabile
Example File http://www.ccarh.org/courses/253/files/midifiles-20080227-2up.pdf
Organization/Parsing file • Beat class • Notes on beat Notes off beat • Beat number • (8)
Chord Identification • Notes: C, E, G • What kind of chord? Look at intervals… • E: m3, m6 -no matches • G: P4, M6 -no matches • C: M3, P5 -These intervals form a major chord, root position
Chord Identification Issue • Non-chord tones: should be ignored in harmonic analysis • Notes in first measure: C, E, G, D • Considers each • possible combination: • CEG • CDE • CDG
Analyzing Data—Machine Learning Approach • Neural Networks: • Each node has a value and an associated weight • In the top layer, inputs become the nodes’ values • Values are propagated through the network, creating values for the other nodes A simple neural network
Learning Algorithm • The network is given a set of training data whose outputs are known Inputs are “fed” through the network: Calculated output is compared with desired output to obtain error http://galaxy.agh.edu.pl/~vlsi/AI/backp_t_en/backprop.html
Learning Algorithm • Back-propagation: the error is propagated backward though the network, and a respective error is calculated for each node • The weights and node values are adjusted based on the errors so that a more desirable output will be obtained http://galaxy.agh.edu.pl/~vlsi/AI/backp_t_en/backprop.html
Learning Algorithm • For my project, the inputs to the network are the types and frequency of chords in a piece of music • A threshold will be set for the output, based on the results of training: different ranges represent different genres