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Music Genre Analysis

Music Genre Analysis. Alex Stabile. Research Questions:. Could a computer learn to distinguish between different composers? Why does music by different composers even sound different?. Possible Answers. Backer et al.: On Musical Stylometry—a Pattern Recognition Approach

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Music Genre Analysis

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  1. Music Genre Analysis Alex Stabile

  2. Research Questions: • Could a computer learn to distinguish between different composers? • Why does music by different composers even sound different?

  3. Possible Answers • Backer et al.: On Musical Stylometry—a Pattern Recognition Approach • Analyzed low-level musical characteristics: note entropy, intervals, rhythms • Used information as input for a statistical model

  4. Project Design • Chords/harmonies all have their own character, so: • Analyze harmonies found in music • Use machine learning techniques to find a relationship between types of harmonies and musical style • Used Python, analyzed Midi files • Compared works by Mozart to works by Rachmaninoff

  5. Example File http://www.ccarh.org/courses/253/files/midifiles-20080227-2up.pdf

  6. Organization/Parsing file • Beat class Notes on beat Notes off beat (Beat number = 8)

  7. 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 C major chord, root position

  8. Analyzing Data—Machine Learning Approach • Neural Networks: • Each node has a value and an associated weight • Top layer is receives input • Values are propagated through the network, creating values for the other nodes A simple neural network

  9. 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

  10. 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

  11. Learning Algorithm—This Project • Inputs to the network are the frequencies of different kinds of chords • Two composers analyzed: Mozart and Rachmaninoff • Expected output for Mozart: 0 • Expected output for Rachmaninoff: 1

  12. Results 4,000 Iterations 10,000 Iterations 14,000 Iterations 20,000 Iterations

  13. Interpretation of Results • Relationship between harmonic content and style/composer • Humans may learn to analyze this subconsciously, but a computer can be trained to do so as well

  14. Future Research • Analyze more musical factors • Analyze more composers • Analyze composers who are more similar (e.g., Mozart and Haydn)

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