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README.md
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Based on the aforementioned original dataset, we conducted data processing to construct the [default subset](#default-subset) of the current integrated version of the dataset. Due to the pre-existing split in the original dataset, wherein the data has been partitioned approximately in a 4:1 ratio for training and testing sets, we uphold the original data division approach for the default subset. The data structure of the default subset can be viewed in the [viewer](https://www.modelscope.cn/datasets/ccmusic-database/GZ_IsoTech/dataPeview). In addition, we have retained the [eval subset](#eval-subset) used in the experiment for easy replication.
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## Viewer
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<https://www.modelscope.cn/datasets/ccmusic-database/GZ_IsoTech/dataPeview>
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## Dataset Structure
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<style>
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.datastructure td {
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<td>8-class</td>
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<td>string</td>
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<td>...</td>
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</table>
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### Data Instances
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### Data Fields
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Categorization of the clips is based on the diverse playing techniques characteristic of the guzheng, the clips are divided into eight categories: Vibrato (chanyin), Upward Portamento (shanghuayin), Downward Portamento (xiahuayin), Returning Portamento (huihuayin), Glissando (guazou, huazhi), Tremolo (yaozhi), Harmonic (fanyin), Plucks (gou, da, mo, tuo…).
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### Data Splits
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train, test
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## Dataset Description
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### Dataset Summary
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Due to the pre-existing split in the raw dataset, wherein the data has been partitioned approximately in a 4:1 ratio for training and testing sets, we uphold the original data division approach. In contrast to utilizing platform-specific automated splitting mechanisms, we directly employ the pre-split data for subsequent integration steps.
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#### Who are the source language producers?
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Students from FD-LAMT
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### Annotations
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#### Annotation process
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This database contains 2824 audio clips of guzheng playing techniques. Among them, 2328 pieces were collected from virtual sound banks, and 496 pieces were played and recorded by a professional guzheng performer.
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#### Who are the annotators?
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Students from FD-LAMT
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## Considerations for Using the Data
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### Social Impact of Dataset
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Promoting the development of the music AI industry
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Based on the aforementioned original dataset, we conducted data processing to construct the [default subset](#default-subset) of the current integrated version of the dataset. Due to the pre-existing split in the original dataset, wherein the data has been partitioned approximately in a 4:1 ratio for training and testing sets, we uphold the original data division approach for the default subset. The data structure of the default subset can be viewed in the [viewer](https://www.modelscope.cn/datasets/ccmusic-database/GZ_IsoTech/dataPeview). In addition, we have retained the [eval subset](#eval-subset) used in the experiment for easy replication.
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## Dataset Structure
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<style>
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.datastructure td {
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<td>8-class</td>
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<td>string</td>
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</tr>
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</table>
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### Data Instances
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### Data Fields
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Categorization of the clips is based on the diverse playing techniques characteristic of the guzheng, the clips are divided into eight categories: Vibrato (chanyin), Upward Portamento (shanghuayin), Downward Portamento (xiahuayin), Returning Portamento (huihuayin), Glissando (guazou, huazhi), Tremolo (yaozhi), Harmonic (fanyin), Plucks (gou, da, mo, tuo…).
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## Dataset Description
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### Dataset Summary
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Due to the pre-existing split in the raw dataset, wherein the data has been partitioned approximately in a 4:1 ratio for training and testing sets, we uphold the original data division approach. In contrast to utilizing platform-specific automated splitting mechanisms, we directly employ the pre-split data for subsequent integration steps.
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#### Who are the source language producers?
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Students from FD-LAMT
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## Considerations for Using the Data
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### Social Impact of Dataset
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Promoting the development of the music AI industry
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