Data
BachChoralHarmony

BachChoralHarmony

active ARFF Publicly available Visibility: public Uploaded 17-02-2016 by Hilda Fabiola Bernard
0 likes downloaded by 2 people , 3 total downloads 0 issues 0 downvotes
Issue #Downvotes for this reason By


Loading wiki
Help us complete this description Edit
Author: -- Creators: Daniele P. Radicioni and Roberto Esposito -- Donor: Daniele P. Radicioni (radicion '@' di.unito.it) and Roberto Esposito (esposito '@' di.unito.it) -- Date: May","2014 Source: UCI Please cite: D. P. Radicioni and R. Esposito. Advances in Music Information Retrieval, chapter BREVE: an HMPerceptron-Based Chord Recognition System. Studies in Computational Intelligence, Zbigniew W. Ras and Alicja Wieczorkowska (Editors), Springer, 2010. Abstract: The data set is composed of 60 chorales (5665 events) by J.S. Bach (1675-1750). Each event of each chorale is labelled using 1 among 101 chord labels and described through 14 features. Source: -- Creators: Daniele P. Radicioni and Roberto Esposito -- Donor: Daniele P. Radicioni (radicion '@' di.unito.it) and Roberto Esposito (esposito '@' di.unito.it) -- Date: May, 2014 Data Set Information: Pitch classes information has been extracted from MIDI sources downloaded from (JSB Chorales)[[Web Link]]. Meter information has been computed through the Meter program which is part of the Melisma music analyser (Melisma)[[Web Link]]. Chord labels have been manually annotated by a human expert. Attribute Information: 1. Choral ID: corresponding to the file names from (Bach Central)[[Web Link]]. 2. Event number: index (starting from 1) of the event inside the chorale. 3-14. Pitch classes: YES/NO depending on whether a given pitch is present. Pitch classes/attribute correspondence is as follows: C -> 3 C#/Db -> 4 D -> 5 ... B -> 14 15. Bass: Pitch class of the bass note 16. Meter: integers from 1 to 5. Lower numbers denote less accented events, higher numbers denote more accented events. 17. Chord label: Chord resonating during the given event. Relevant Papers: 1. D. P. Radicioni and R. Esposito. Advances in Music Information Retrieval, chapter BREVE: an HMPerceptron-Based Chord Recognition System. Studies in Computational Intelligence, Zbigniew W. Ras and Alicja Wieczorkowska (Editors), Springer, 2010. 2. Esposito, R. and Radicioni, D. P., CarpeDiem: Optimizing the Viterbi Algorithm and Applications to Supervised Sequential Learning, Journal of Machine Learning Research, 10(Aug):1851-1880, 2009. Citation Request: D. P. Radicioni and R. Esposito. Advances in Music Information Retrieval, chapter BREVE: an HMPerceptron-Based Chord Recognition System. Studies in Computational Intelligence, Zbigniew W. Ras and Alicja Wieczorkowska (Editors), Springer, 2010.

17 features

V17 (target)nominal102 unique values
0 missing
V1nominal62 unique values
0 missing
V2numeric207 unique values
0 missing
V3nominal2 unique values
0 missing
V4nominal2 unique values
0 missing
V5nominal2 unique values
0 missing
V6nominal2 unique values
0 missing
V7nominal2 unique values
0 missing
V8nominal2 unique values
0 missing
V9nominal2 unique values
0 missing
V10nominal2 unique values
0 missing
V11nominal2 unique values
0 missing
V12nominal2 unique values
0 missing
V13nominal2 unique values
0 missing
V14nominal2 unique values
0 missing
V15nominal16 unique values
0 missing
V16numeric5 unique values
0 missing

62 properties

5665
Number of instances (rows) of the dataset.
17
Number of attributes (columns) of the dataset.
102
Number of distinct values of the target attribute (if it is nominal).
0
Number of missing values in the dataset.
0
Number of instances with at least one value missing.
2
Number of numeric attributes.
15
Number of nominal attributes.
53.37
Third quartile of means among attributes of the numeric type.
1.81
Maximum mutual information between the nominal attributes and the target attribute.
2
The minimal number of distinct values among attributes of the nominal type.
11.76
Percentage of numeric attributes.
0.57
Third quartile of mutual information between the nominal attributes and the target attribute.
102
The maximum number of distinct values among attributes of the nominal type.
0.38
Minimum skewness among attributes of the numeric type.
88.24
Percentage of nominal attributes.
0.93
Third quartile of skewness among attributes of the numeric type.
0.93
Maximum skewness among attributes of the numeric type.
1.11
Minimum standard deviation of attributes of the numeric type.
0.67
First quartile of entropy among attributes.
37.27
Third quartile of standard deviation of attributes of the numeric type.
37.27
Maximum standard deviation of attributes of the numeric type.
0.02
Percentage of instances belonging to the least frequent class.
-0.93
First quartile of kurtosis among attributes of the numeric type.
29
Standard deviation of the number of distinct values among attributes of the nominal type.
1.36
Average entropy of the attributes.
1
Number of instances belonging to the least frequent class.
3.13
First quartile of means among attributes of the numeric type.
-0.04
Mean kurtosis among attributes of the numeric type.
12
Number of binary attributes.
0.48
First quartile of mutual information between the nominal attributes and the target attribute.
28.25
Mean of means among attributes of the numeric type.
0.38
First quartile of skewness among attributes of the numeric type.
0.45
Average class difference between consecutive instances.
0.68
Average mutual information between the nominal attributes and the target attribute.
1.11
First quartile of standard deviation of attributes of the numeric type.
4.97
Entropy of the target attribute values.
0.99
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
0.9
Second quartile (Median) of entropy among attributes.
0
Number of attributes divided by the number of instances.
13.6
Average number of distinct values among the attributes of the nominal type.
-0.04
Second quartile (Median) of kurtosis among attributes of the numeric type.
7.26
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
0.65
Mean skewness among attributes of the numeric type.
28.25
Second quartile (Median) of means among attributes of the numeric type.
8.88
Percentage of instances belonging to the most frequent class.
19.19
Mean standard deviation of attributes of the numeric type.
0.55
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
503
Number of instances belonging to the most frequent class.
0.52
Minimal entropy among attributes.
0.65
Second quartile (Median) of skewness among attributes of the numeric type.
5.83
Maximum entropy among attributes.
-0.93
Minimum kurtosis among attributes of the numeric type.
70.59
Percentage of binary attributes.
19.19
Second quartile (Median) of standard deviation of attributes of the numeric type.
0.98
Third quartile of entropy among attributes.
0.84
Maximum kurtosis among attributes of the numeric type.
3.13
Minimum of means among attributes of the numeric type.
0
Percentage of instances having missing values.
0.84
Third quartile of kurtosis among attributes of the numeric type.
53.37
Maximum of means among attributes of the numeric type.
0.32
Minimal mutual information between the nominal attributes and the target attribute.
0
Percentage of missing values.

5 tasks

31 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: V17
0 runs - estimation_procedure: 33% Holdout set - target_feature: V17
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
Define a new task