Introduction
Literature review
Feature extraction
Data extraction
Feature generation
Illustrative examples
Onset (beats) | Midi pitch | Duration (s) |
---|---|---|
3 | 62 | 0.3271 |
3.75 | 62 | 0.1123 |
4 | 67 | 1.2939 |
7 | 69 | 0.3271 |
7.75 | 69 | 0.1123 |
8 | 71 | 0.4346 |
9 | 62 | 0.3271 |
9.75 | 62 | 0.1123 |
10 | 64 | 0.4346 |
11 | 66 | 0.3271 |
11.75 | 66 | 0.1123 |
12 | 67 | 0.4346 |
13 | 62 | 0.1465 |
13.333 | 62 | 0.1465 |
13.667 | 62 | 0.1465 |
14 | 67 | 0.4346 |
Absolute pitch | Relative pitch | 3-grams | Occurrence |
---|---|---|---|
62 | – | – | – |
62 | 0 | – | – |
67 | 5 | – | – |
69 | 2 | ‘0 5 2’ | 1 |
69 | 0 | ‘5 2 0’ | 1 |
71 | 2 | ‘2 0 2’ | 1 |
62 | −9 | ‘0 2 −9’ | 1 |
62 | 0 | ‘2 −9 0’ | 1 |
64 | 2 | ‘−9 0 2’ | 1 |
66 | 2 | ‘0 2 2’ | 1 |
66 | 0 | ‘2 2 0’ | 1 |
67 | 1 | ‘2 0 1’ | 1 |
62 | −5 | ‘0 1 −5’ | 1 |
62 | 0 | ‘1 −5 0’ | 1 |
62 | 0 | ‘−5 0 0’ | 1 |
67 | 5 | ‘0 0 5’ | 1 |
Absolute duration | Relative duration | 3-grams | Occurrence |
---|---|---|---|
0.327148 | – | – | – |
0.112305 | −1.5 | – | – |
1.293945 | 3.5 | – | – |
0.327148 | −2 | ‘−1.5 3.5 −2’ | 1 |
0.112305 | −1.5 | ‘3.5 −2 −1.5’ | 1 |
0.43457 | 2 | ‘−2 −1.5 2’ | 1 |
0.327148 | −0.4 | ‘−1.5 2 −0.4’ | 2 |
0.112305 | −1.5 | ‘2 −0.4 −1.5’ | 2 |
0.43457 | 2 | ‘−0.4 −1.5 2’ | 2 |
0.327148 | −0.4 | – | – |
0.112305 | −1.5 | – | – |
0.43457 | 2 | – | – |
0.146484 | −1.6 | ‘−1.5 2 −1.6’ | 1 |
0.146484 | 0 | ‘2 −1.6 0’ | 1 |
0.146484 | 0 | ‘−1.6 0 0’ | 1 |
0.43457 | 1.6 | ‘0 0 1.6’ | 1 |
Rhythm features | Occurrence |
---|---|
Complete beats | 10 |
Half beats | 0 |
Quarter beats | 4 |
1/8th beats | 0 |
1/3rd beats | 2 |
Cortical algorithm for feature reduction and classification
Primer on cortical algorithms
Cortical algorithm for feature reduction
Experimental results
Dataset
Composer | Area | No. of files | Total size (MB) | Total number of beats |
---|---|---|---|---|
Bach | Baroque | 246 | 4.3 | 179,259 |
Beethoven | Classical-romantic | 185 | 10.6 | 490,485 |
Chopin | Romantic | 87 | 2.13 | 97,355 |
Corelli | Baroque | 246 | 3.6 | 146,009 |
Haydn | Classical | 212 | 7.5 | 338,763 |
Joplin | Ragtime | 45 | 1.37 | 66,043 |
Mozart | Classical | 82 | 3.36 | 151,912 |
Scarlatti | Baroque | 59 | 1.3 | 60,849 |
Vivaldi | Baroque | 35 | 1.23 | 55,544 |
Experiments
-
Experiment 1: Using our proposed feature vector, and our feature reduction scheme (CA-FR).
-
Experiment 2: Using only the n-grams features (as widely proposed in the literature) and our feature reduction algorithm (CA-FR).
-
Experiment 3: Using the entire feature vector proposed and a principal component analysis (PCA) for feature reduction.
-
Experiment 4: Using only the n-grams features and a PCA for feature reduction.
Reference | Task | Recognition rate |
---|---|---|
[17] | Bach vs. Haydn | 96.8 |
Bach vs. Mozart | 97.5 | |
Haydn vs. Mozart | 74.7 | |
[20] | Bach vs. Beethoven | 81.2 |
Bach vs. Chopin | 87.0 | |
Bach vs. Haydn | 69.3 | |
Bach vs. Mozart | 82.2 | |
Beethoven vs. Chopin | 63.8 | |
Beethoven vs. Haydn | 65.0 | |
Beethoven vs. Mozart | 69.8 | |
Chopin vs. Haydn | 77.8 | |
Chopin vs. Mozart | 77.0 | |
Haydn vs. Mozart | 55.8 | |
[18] | Bach vs. not Bach | 93.1 |
Beethoven vs. not Beethoven | 63.3 | |
Chopin vs. not Chopin | 98.11 | |
Haydn vs. not Haydn | 40 | |
Mozart vs. not Mozart | 74.6 | |
Vivaldi vs. not Vivaldi | 87.0 |
Exp. | Classifier | % of features | |||||
---|---|---|---|---|---|---|---|
100 | 80 | 50 | 20 | 5 | 0.1 | ||
Exp. 1 | CA | 76.6 | 83.5 | 90.6 | 92.3 | 92.6 |
94.9
|
SVM | 67.8 | 69 | 69.5 | 70.8 | 72 | 72.8 | |
1NN | 57.9 | 59.8 | 60.5 | 61 | 62.2 | 62.2 | |
Exp. 2 | CA | 73.6 | 78.5 | 83.6 | 88.7 | 90.5 |
91.1
|
SVM | 61.6 | 64.5 | 65.4 | 67.8 | 65.8 | 67.3 | |
1NN | 53.8 | 56 | 57.4 | 57.1 | 57.2 | 56.4 | |
Exp. 3 | CA | 76 | 77.7 | 82.8 | 88.6 | 89.9 |
90.4
|
SVM | 69.6 | 68.1 | 69.1 | 71.4 | 69.7 | 70.5 | |
1NN | 61 | 59.9 | 61 | 61.1 | 61 | 60.1 | |
Exp. 4 | CA | 73.8 | 76.3 | 78.5 | 87.1 | 87.8 |
86.4
|
SVM | 61.9 | 63.5 | 66.3 | 68.2 | 66.3 | 64.9 | |
1NN | 54.3 | 53.4 | 58.8 | 59.4 | 58.3 | 57.7 |
Exp. | Classifier | % of features | |||||
---|---|---|---|---|---|---|---|
100 | 80 | 50 | 20 | 5 | 0.1 | ||
Exp. 1 | CA | 180 | 135 | 80 | 55 | 37 | 21 |
SVM | 28 | 25 | 22 | 18 | 13 | 9 | |
Exp. 2 | CA | 176 | 127 | 76 | 51 | 33 | 17 |
SVM | 24 | 17 | 14 | 10 | 9 | 1 | |
Exp 3 | CA | 240 | 210 | 110 | 130 | 82 | 96 |
SVM | 103 | 85 | 52 | 93 | 73 | 39 | |
Exp. 4 | CA | 228 | 200 | 102 | 120 | 70 | 86 |
SVM | 93 | 73 | 40 | 85 | 65 | 31 |
Corelli | Vivaldi | Bach | Scarlatti | Haydn | Mozart | Beethoven | Chopin | Joplin | |
---|---|---|---|---|---|---|---|---|---|
Corelli (1653) | – | 94.8 | 94.3 | 95.5 | 97.5 | 96.8 | 92.4 | 99.2 | 98.8 |
Vivaldi (1678) | – | 96.4 | 93.9 | 97 | 96.5 | 98.2 | 95.1 | 99.5 | |
Bach (1685) | – | 95.2 | 97.8 | 98.7 | 97.1 | 96.5 | 98.9 | ||
Scarlatti (1685) | – | 96.7 | 96.8 | 96.3 | 95.5 | 99.2 | |||
Haydn (1732) | – | 82.9 | 90.7 | 98.6 | 99.1 | ||||
Mozart (1756) | – | 87.9 | 94.7 | 98.7 | |||||
Beethoven (1770) | – | 86.1 | 97.6 | ||||||
Chopin (1810) | – | 98.7 | |||||||
Joplin (1868) | – |
Metric | Exp. 1 | Exp. 2 | Exp. 3 | Exp. 4 | ||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
CA | SVM | 1NN | CA | SVM | 1NN | CA | SVM | 1NN | CA | SVM | 1NN | |
Mean | 88.4 | 70.3 | 60.6 | 84.3 | 65.4 | 56.3 | 84.2 | 69.7 | 60.7 | 81.7 | 65.2 | 57 |
Std. | 7 | 1.9 | 1.6 | 7.1 | 2.2 | 1.3 | 6.4 | 1.1 | 0.5 | 6.2 | 2.2 | 2.5 |
Max | 94.9 | 72.8 | 62.2 | 91.1 | 67.8 | 57.4 | 90.4 | 71.4 | 61.1 | 87.8 | 68.2 | 59.4 |
Min | 76.6 | 67.8 | 57.9 | 73.6 | 61.6 | 53.8 | 76 | 68.1 | 59.9 | 73.8 | 61.9 | 53.4 |