Update README.md

README.md

@@ -118,9 +118,8 @@ Based on the aforementioned original dataset, we conducted data processing to co
 ### Data Fields
 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…).
 
-## Dataset Description
 ### Dataset Summary
-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.
+A dataset for Guzheng playing technique classification
 
 ### Statistics
 | ![](https://www.modelscope.cn/datasets/ccmusic-database/GZ_IsoTech/resolve/master/data/gziso_pie.jpg) | ![](https://www.modelscope.cn/datasets/ccmusic-database/GZ_IsoTech/resolve/master/data/gziso.jpg) | ![](https://www.modelscope.cn/datasets/ccmusic-database/GZ_IsoTech/resolve/master/data/gziso_bar.jpg) |
@@ -179,28 +178,6 @@ GIT_LFS_SKIP_SMUDGE=1 git clone git@hf.co:datasets/ccmusic-database/CNPM
 cd CNPM
 ```
 
-## Dataset Creation
-### Curation Rationale
-The Guzheng is a kind of traditional Chinese instrument with diverse playing techniques. Instrument playing techniques (IPT) play an important role in musical performance. However, most of the existing works for IPT detection show low efficiency for variable-length audio and do not assure generalization as they rely on a single sound bank for training and testing. In this study, we propose an end-to-end Guzheng playing technique detection system using Fully Convolutional Networks that can be applied to variable-length audio. Because each Guzheng playing technique is applied to a note, a dedicated onset detector is trained to divide an audio into several notes and its predictions are fused with frame-wise IPT predictions. During fusion, we add the IPT predictions frame by frame inside each note and get the IPT with the highest probability within each note as the final output of that note. We create a new dataset named GZ_IsoTech from multiple sound banks and real-world recordings for Guzheng performance analysis. Our approach achieves 87.97% in frame-level accuracy and 80.76% in note-level F1 score, outperforming existing works by a large margin, which indicates the effectiveness of our proposed method in IPT detection.
-
-### Source Data
-#### Initial Data Collection and Normalization
-Dichucheng Li, Monan Zhou
-
-#### Who are the source language producers?
-Students from FD-LAMT
-
-## Considerations for Using the Data
-### Social Impact of Dataset
-Promoting the development of the music AI industry
-
-### Discussion of Biases
-Only for Traditional Chinese Instruments
-
-### Other Known Limitations
-Insufficient sample 
-
-## Additional Information
 ### Dataset Curators
 Dichucheng Li