Abstract
This paper explores the classification of metric types using different feature representations. Using weighted timepoint, DFT, and autocorrelation, we train feedforward neural networks to distinguish allemandes, courantes, sarabandes, and gavottes in the Yale-Classical Archives Corpus. Autocorrelation and DFT models perform better than a baseline, with DFT consistently better by a small amount.
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Notes
- 1.
For \(k > N/2\), \(F_k(X)\) and \(F_{N-k}(X)\) have equal magnitude and opposite phase for a real-valued signal, X, by the aliasing principle.
- 2.
We include only even values here, because the zero padding produces distracting artifacts in the odd coefficients.
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Chiu, M., Yust, J. (2022). Identifying Metric Types with Optimized DFT and Autocorrelation Models. In: Montiel, M., AgustÃn-Aquino, O.A., Gómez, F., Kastine, J., Lluis-Puebla, E., Milam, B. (eds) Mathematics and Computation in Music. MCM 2022. Lecture Notes in Computer Science(), vol 13267. Springer, Cham. https://doi.org/10.1007/978-3-031-07015-0_28
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