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

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Presentation transcript:

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 Analyzed low-level musical characteristics: note entropy, intervals, rhythms Used information as input for a statistical model

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

Example File

Organization/Parsing file Beat class Notes on beatNotes 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 C major chord, root position

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

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

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

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

Results 4,000 Iterations10,000 Iterations 14,000 Iterations20,000 Iterations

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

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