Identifying Metric Types with Optimized DFT and Autocorrelation Models

被引:0
作者
Chiu, Matt [1 ]
Yust, Jason [2 ]
机构
[1] Eastman Sch Mus, Rochester, NY 14604 USA
[2] Boston Univ, Boston, MA 02215 USA
来源
MATHEMATICS AND COMPUTATION IN MUSIC (MCM 2022) | 2022年
关键词
Discrete Fourier transform; Autocorrelation; Meter classification; Metric types; Neural networks; METER;
D O I
10.1007/978-3-031-07015-0_28
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
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.
引用
收藏
页码:343 / 348
页数:6
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