Firm-Teacher_Clave-Direction_Classification
Donated on 4/23/2015
The data are binary attack-point vectors and their clave-direction class(es) according to the partido-alto-based paradigm.
Dataset Characteristics
Multivariate
Subject Area
Other
Associated Tasks
Classification
Feature Type
-
# Instances
10800
# Features
20
Dataset Information
Additional Information
The data consist of 16 binary inputs and one 'four-bit' one-hot classification output. The 16-bit inputs are binary-valued attack-point vectors. 1 indicates the substantial presence (0, absence) of an onset (note start) in a certain time window during one bar of 4/4 time music (not limited to percussion, hence *onset* vectors without duration) quantized to 16th-note subdivisions. Each vector has 16 positions in which there may be or not be an onset. The output classes (left to right: neutral, reverse clave, forward clave, and incoherent) were determined through the music-theoretic/ethnomusicological portion of the my dissertation studies, based on both double-blind listening tests and informal interviews with with four professional master-musicians, as well as decades of studying the music. Future uploads (subject only to formatting) can include an additional column of fuzzy descriptors (of the degree of match to the output class).
Has Missing Values?
No
Variable Information
In terms of divisive rhythm counting, the first 16 attributes (input bits) correspond to a significant onset at: 1 e & a 2 e & a 3 e & a 4 e & a of one bar of 4/4 time. The last four are the output classes (3 - neutral, 2 - reverse clave, 1 - forward clave, 0 - incoherent) in one-hot (one-up) encoding.
Dataset Files
| File | Size |
|---|---|
| ClaveVectors_Firm-Teacher_Model.txt | 432.6 KB |
Reviews
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pip install ucimlrepo
from ucimlrepo import fetch_ucirepo # fetch dataset firm_teacher_clave_direction_classification = fetch_ucirepo(id=324) # data (as pandas dataframes) X = firm_teacher_clave_direction_classification.data.features y = firm_teacher_clave_direction_classification.data.targets # metadata print(firm_teacher_clave_direction_classification.metadata) # variable information print(firm_teacher_clave_direction_classification.variables)
Vurka, M. (2011). Firm-Teacher_Clave-Direction_Classification [Dataset]. UCI Machine Learning Repository. https://doi.org/10.24432/C5GC9F.
Creators
Mehmet Vurka
DOI
License
This dataset is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.
This allows for the sharing and adaptation of the datasets for any purpose, provided that the appropriate credit is given.