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BachChoralHarmony

BachChoralHarmony

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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
V9nominal2 unique values
0 missing
V16numeric5 unique values
0 missing
V15nominal16 unique values
0 missing
V14nominal2 unique values
0 missing
V13nominal2 unique values
0 missing
V12nominal2 unique values
0 missing
V11nominal2 unique values
0 missing
V10nominal2 unique values
0 missing
V1nominal62 unique values
0 missing
V8nominal2 unique values
0 missing
V7nominal2 unique values
0 missing
V6nominal2 unique values
0 missing
V5nominal2 unique values
0 missing
V4nominal2 unique values
0 missing
V3nominal2 unique values
0 missing
V2numeric207 unique values
0 missing
Show all 17 features

107 properties

NumberOfInstances
5665
Number of instances (rows) of the dataset.
NumberOfFeatures
17
Number of attributes (columns) of the dataset.
NumberOfClasses
102
Number of distinct values of the target attribute (if it is nominal).
NumberOfMissingValues
0
Number of missing values in the dataset.
NumberOfInstancesWithMissingValues
0
Number of instances with at least one value missing.
NumberOfNumericFeatures
2
Number of numeric attributes.
NumberOfSymbolicFeatures
15
Number of nominal attributes.
AutoCorrelation
0.45
Average class difference between consecutive instances.
CfsSubsetEval_DecisionStumpAUC
0.71
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
CfsSubsetEval_DecisionStumpErrRate
0.84
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
CfsSubsetEval_DecisionStumpKappa
0.08
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
CfsSubsetEval_NaiveBayesAUC
0.98
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
CfsSubsetEval_NaiveBayesErrRate
0.27
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
CfsSubsetEval_NaiveBayesKappa
0.72
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
CfsSubsetEval_kNN1NAUC
0.94
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
CfsSubsetEval_kNN1NErrRate
0.26
Error rate achieved by the landmarker weka.classifiers.lazy.IBk -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
CfsSubsetEval_kNN1NKappa
0.73
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
ClassEntropy
4.97
Entropy of the target attribute values.
DecisionStumpAUC
0.71
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
DecisionStumpErrRate
0.84
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
DecisionStumpKappa
0.08
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
Dimensionality
0
Number of attributes divided by the number of instances.
EquivalentNumberOfAtts
7.26
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
J48.00001.AUC
0.93
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.00001.ErrRate
0.35
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.00001.Kappa
0.63
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.0001.AUC
0.93
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.0001.ErrRate
0.35
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.0001.Kappa
0.63
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.001.AUC
0.93
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
J48.001.ErrRate
0.34
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
J48.001.Kappa
0.64
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
MajorityClassPercentage
8.88
Percentage of instances belonging to the most frequent class.
MajorityClassSize
503
Number of instances belonging to the most frequent class.
MaxAttributeEntropy
5.83
Maximum entropy among attributes.
MaxKurtosisOfNumericAtts
0.84
Maximum kurtosis among attributes of the numeric type.
MaxMeansOfNumericAtts
53.37
Maximum of means among attributes of the numeric type.
MaxMutualInformation
1.81
Maximum mutual information between the nominal attributes and the target attribute.
MaxNominalAttDistinctValues
102
The maximum number of distinct values among attributes of the nominal type.
MaxSkewnessOfNumericAtts
0.93
Maximum skewness among attributes of the numeric type.
MaxStdDevOfNumericAtts
37.27
Maximum standard deviation of attributes of the numeric type.
MeanAttributeEntropy
1.36
Average entropy of the attributes.
MeanKurtosisOfNumericAtts
-0.04
Mean kurtosis among attributes of the numeric type.
MeanMeansOfNumericAtts
28.25
Mean of means among attributes of the numeric type.
MeanMutualInformation
0.68
Average mutual information between the nominal attributes and the target attribute.
MeanNoiseToSignalRatio
0.99
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
MeanNominalAttDistinctValues
13.6
Average number of distinct values among the attributes of the nominal type.
MeanSkewnessOfNumericAtts
0.65
Mean skewness among attributes of the numeric type.
MeanStdDevOfNumericAtts
19.19
Mean standard deviation of attributes of the numeric type.
MinAttributeEntropy
0.52
Minimal entropy among attributes.
MinKurtosisOfNumericAtts
-0.93
Minimum kurtosis among attributes of the numeric type.
MinMeansOfNumericAtts
3.13
Minimum of means among attributes of the numeric type.
MinMutualInformation
0.32
Minimal mutual information between the nominal attributes and the target attribute.
MinNominalAttDistinctValues
2
The minimal number of distinct values among attributes of the nominal type.
MinSkewnessOfNumericAtts
0.38
Minimum skewness among attributes of the numeric type.
MinStdDevOfNumericAtts
1.11
Minimum standard deviation of attributes of the numeric type.
MinorityClassPercentage
0.02
Percentage of instances belonging to the least frequent class.
MinorityClassSize
1
Number of instances belonging to the least frequent class.
NaiveBayesAUC
0.98
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NaiveBayesErrRate
0.27
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NaiveBayesKappa
0.72
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NumberOfBinaryFeatures
12
Number of binary attributes.
PercentageOfBinaryFeatures
70.59
Percentage of binary attributes.
PercentageOfInstancesWithMissingValues
0
Percentage of instances having missing values.
PercentageOfMissingValues
0
Percentage of missing values.
PercentageOfNumericFeatures
11.76
Percentage of numeric attributes.
PercentageOfSymbolicFeatures
88.24
Percentage of nominal attributes.
Quartile1AttributeEntropy
0.67
First quartile of entropy among attributes.
Quartile1KurtosisOfNumericAtts
-0.93
First quartile of kurtosis among attributes of the numeric type.
Quartile1MeansOfNumericAtts
3.13
First quartile of means among attributes of the numeric type.
Quartile1MutualInformation
0.48
First quartile of mutual information between the nominal attributes and the target attribute.
Quartile1SkewnessOfNumericAtts
0.38
First quartile of skewness among attributes of the numeric type.
Quartile1StdDevOfNumericAtts
1.11
First quartile of standard deviation of attributes of the numeric type.
Quartile2AttributeEntropy
0.9
Second quartile (Median) of entropy among attributes.
Quartile2KurtosisOfNumericAtts
-0.04
Second quartile (Median) of kurtosis among attributes of the numeric type.
Quartile2MeansOfNumericAtts
28.25
Second quartile (Median) of means among attributes of the numeric type.
Quartile2MutualInformation
0.55
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
Quartile2SkewnessOfNumericAtts
0.65
Second quartile (Median) of skewness among attributes of the numeric type.
Quartile2StdDevOfNumericAtts
19.19
Second quartile (Median) of standard deviation of attributes of the numeric type.
Quartile3AttributeEntropy
0.98
Third quartile of entropy among attributes.
Quartile3KurtosisOfNumericAtts
0.84
Third quartile of kurtosis among attributes of the numeric type.
Quartile3MeansOfNumericAtts
53.37
Third quartile of means among attributes of the numeric type.
Quartile3MutualInformation
0.57
Third quartile of mutual information between the nominal attributes and the target attribute.
Quartile3SkewnessOfNumericAtts
0.93
Third quartile of skewness among attributes of the numeric type.
Quartile3StdDevOfNumericAtts
37.27
Third quartile of standard deviation of attributes of the numeric type.
REPTreeDepth1AUC
0.84
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth1ErrRate
0.74
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth1Kappa
0.21
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth2AUC
0.86
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth2ErrRate
0.53
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth2Kappa
0.44
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth3AUC
0.85
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
REPTreeDepth3ErrRate
0.51
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
REPTreeDepth3Kappa
0.46
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
RandomTreeDepth1AUC
0.72
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth1ErrRate
0.84
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth1Kappa
0.08
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth2AUC
0.85
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth2ErrRate
0.68
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth2Kappa
0.26
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth3AUC
0.88
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
RandomTreeDepth3ErrRate
0.54
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
RandomTreeDepth3Kappa
0.42
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
StdvNominalAttDistinctValues
29
Standard deviation of the number of distinct values among attributes of the nominal type.
kNN1NAUC
0.86
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
kNN1NErrRate
0.27
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
kNN1NKappa
0.72
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
Show all 107 properties

13 tasks

Supervised Classification on BachChoralHarmony
31 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: V17
Supervised Classification on BachChoralHarmony
0 runs - estimation_procedure: 33% Holdout set - target_feature: V17
Clustering on BachChoralHarmony
0 runs
Clustering on BachChoralHarmony
0 runs - estimation_procedure: 50 times Clustering
Clustering on BachChoralHarmony
0 runs - estimation_procedure: 50 times Clustering
Clustering on BachChoralHarmony
0 runs - estimation_procedure: 50 times Clustering
Clustering on BachChoralHarmony
0 runs - estimation_procedure: 50 times Clustering
Clustering on BachChoralHarmony
0 runs - estimation_procedure: 50 times Clustering
Clustering on BachChoralHarmony
0 runs - estimation_procedure: 50 times Clustering
Clustering on BachChoralHarmony
0 runs - estimation_procedure: 50 times Clustering
Clustering on BachChoralHarmony
0 runs - estimation_procedure: 50 times Clustering
Clustering on BachChoralHarmony
0 runs - estimation_procedure: 50 times Clustering
Clustering on BachChoralHarmony
0 runs - estimation_procedure: 50 times Clustering
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