SNU Dataset for Online Music Symbol Recognition

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- SNU dataset
- The labels of strokes in SNU dataset

SNU dataset

For research on the recognition of online handwritten music symbols the SNU dataset provides handwritten music symbol samples which were drawn by 18 musicians. Similar to the HOMUS dataset<ref>J. Calvo-Zaragoza and J. Oncina, Recognition of Pen-Based Music Notation: the HOMUS dataset, ICPR 2014.</ref>, the SNU dataset consists of total 1716 symbol samples and the symbol set in SNU dataset is a subset of HOMUS dataset. Each symbol sample in the SNU dataset, like the HOMUS dataset, consists of at least one stroke and a stroke is defined as a sequence of two dimensional points, which are the successive locations of a stylus pen on a device in time sequence while the pen touches the device. The symbols in the SNU dateset are a subset of the HOMUS dataset. There are 16 musical symbols in SNU dataset which is summarized in Table 1 and Table 2. Each directory contains text files which were contain handwritten music symbol data drawn by each musician.
The text files consist of

- Symbol Name
- Sets of 2D points representing each stroke seperated by a semicolon.

An example of a text file:

1. Natural
2. 482.22,409.96;481.84,411.24;481.19,414.07;480.81,419.50;480.38,425.88;480.17,436.76;481.06,438.69;484.66,439.67;488.73,439.41;493.95,438.00;496.18,436.89;496.01,435.99;496.01,435.99;
3. 482.56,418.09;482.56,418.09;483.29,418.73;485.30,420.19;488.56,421.77;492.03,423.53;495.19,427.13;493.61,435.52;491.60,439.97;490.87,454.14;490.78,460.31;492.07,470.37;492.07,470.37;

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Figure 1. Examples of 16 symbols in SNU dataset

Table 1. Types of musical symbols in the SNU dataset
Note whole, half, quarter, eighth, sixteenth
Rest whole/half, quarter, eighth, sixteenth
Accidentals flat, sharp, natural
Clef G-clef, F-clef
Others dot, barline

Table 2. The number of each symbol in Fig. 1
Symbol Num. of symboles Symbol Num. of symboles
1 BarLine 263 9 Natural 80
2 Dot 81 10 Quarter-Note 108
3 Eighth-Note 142 11 Quarter-Rest 90
4 Eighth-Rest 71 12 Sharp 111
5 F-Clef 90 13 Sixteenth-Note 140
6 Flat 88 14 Sixteenth-Rest 72
7 G-Clef 147 15 Whole-Half-Rest 54
8 Half-Note 108 16 Whole-Note 71

In the choice, we tried to define as small number of the basic strokes as possible keeping the similarity between any pair of the strokes to be as small as possble, and each basic stroke was enforced to contain somewhat large variations. With those basis strokes, we labeled all the strokes as one of the twenty four classes, which is summarized in Table 1. Note that most samples corresponding to 24 symbols in Fig. 1 can be represented in the combinations of the basic strokes in Fig. 2.

The labels of strokes in SNU dataset

In the same manner as described at Labels of Strokes in HOMUS Dataset the SNU dataset was labeled.<ref>H. Miyao and M. Maruyama, An online handwritten music symbol recognition system, International Journal of Document Analysis and Recognition, vol. 9, pp. 49-58, 2007.</ref> The difference is that basic stroke set included in SNU dataset is smaller than it in HOMUS dataset. Same as symbol set in SNU dataset the stroke set is a subset of the HOMUS dataset. Table 3. shows more detail. Also there are no time signature in the SNU dataset the labels doesn't include 100.
The label files consist of

- Symbol Name
- Label number of each stroke

An example of a label file:

1. Natural
2. 10
3. 21

Alt text
Figure 2. Examples of 19 basic strokes in SNU dataset

Table 3. The number of each stroke in Fig. 2
Stroke label Stroke Num. of strokes Stroke label Stroke Num. of strokes
0 None 75 12 RestArc 52
1 VLine 914 13 RestArc2 10
2 HLine 141 14 QRest 90
3 CommonTimeArc 0 15 Fill 52
4 Dot 268 16 WRest 6
5 WHead 174 17 GClef 141
6 BHead 357 18 FClefArc 98
7 LSlash 303 19 CClef2Arc 0
8 RSlash 151 20 RevNaturalRt 0
9 StUHook 1 21 NaturalRt 79
10 StLHook 119 22 Lightning 0
11 8Rest 150 23 Flat 81

The SNU dataset and its label were used in our paper<ref>J. Oh, S. J. Son, S. Lee, J.-W. Kwon, and N. Kwak, Online Recognition of Handwritten Music Symbols, International Journal of Document Analysis and Recognition, vol. 20, pp. 79-89, June 2017.</ref>.


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Sung Joon Son, Ph.D. candidate, E-mail: sjson718_at_snu_dot_ac_dot_kr