Labels of strokes in HOMUS Dataset
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The Handwritten Online Musical Symbols (HOMUS) dataset<ref>J. Calvo-Zaragoza and J. Oncina, Recognition of Pen-Based Music Notation: the HOMUS dataset, ICPR 2014.</ref> consists of 15200 musical symbol samples collected from 100 musicians. Each sample belongs to one of 32 symbols. It can be download at the dataset homepage. Each symbol sample in this 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. Nonetheless, the dataset does not serve labels corresponding to the strokes of symbols in the dataset. Excluding 3200 symbol samples corresponding to 8 symbols of time signatures, we analyzed all of 31768 strokes in the other samples for 24 symbols as shown in Fig. 1.
As a result, we chose 23 basic strokes such that most samples corresponding to 24 symbols in Fig. 1 can be represented in the combinations of them. Figure 2 shows the basic strokes and Table 1 summaries the strokes, their class number, and the numbers of stroke samples in the subset of HOMUS dataset. In Table 1, None stroke means the strokes that can not be categorized into any of the 23 basic strokes.
Download the labels of strokes in HOMUS dataset (v1.0)
Each data file contains a name of symbol and the labels of its strokes in a similar manner that a symbol sample are written in a file. The only difference is that the stroke labels are included in the files instead of raw data of strokes. For consistency, the strokes of symbol samples corresponding to 8 time signatures are commonly labeled as 100.
|Stroke label||Stroke||# of strokes||Stroke label||Stroke||# of strokes|
We have developed an algorithm for online handwritten musical symbol recognition using the labels. The algorithm is specifically described in our paper<ref>J. Oh, S. J. Son, S. Lee, and N. Kwak, Online Recognition of Handwritten Music Symbols, to be submitted.</ref>.
Sung Joon Son, Ph.D. candidate, E-mail: sjson718_at_snu_dot_ac_dot_kr