Rhythmic Research > Eigenrhythms

EIGENRHYTHMS:
DRUM PATTERN BASIS SETS FOR CLASSIFICATION AND GENERATION

Daniel P.W. Ellis and John Arroyo
LabROSA, Dept. of Elec. Eng., Columbia University, NY NY USA

We took a collection of 100 drum beats from popular music tracks and estimated the measure length and downbeat position of each one. Using these values, we normal-ized each pattern to form an ensemble of aligned drum patterns. Principal Component Analysis on this data set results in a set of basis ‘patterns’ that can be combined to give approximations and interpolations of all the examples. We use this low dimension representation of the drum patterns as a space for classification and visualization, and discuss its application to generating continua of rhythms. Our classification results were very modest - about 20% correct on a 10-way genre classification task - but we show that the projection into principal component space reveals aspects of the rhythm that are largely orthogonal to genre but are still perceptually relevant.

Introduction >>

Eigenrhythms

• Introduction
• Eigenrhythms
• Method
• Results
• Conclusion
• References

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