1. Music Genre Classification Alex Stabile

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

3. Organization/Parsing file Beat class – Notes on beatNotes off beat – Beat number (8)

4. 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

5. 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

6. 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

7. 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

8. 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

9. 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