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;

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

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>.

References

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Contact

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