Update README.md

README.md

@@ -89,7 +89,7 @@ train, validation, test
 The integrated version provides the original content and the spectrogram generated in the experimental part of the paper cited above. For the second part, the pre-process in the paper is replicated. Each audio clip is a 3-second segment sampled at 44,100Hz, which is subsequently converted into a log Constant-Q Transform (CQT) spectrogram. A CQT accompanied by a label constitutes a single data entry, forming the first and second columns, respectively. The CQT is a 3-dimensional array with the dimension of 88 x 258 x 1, representing the frequency-time structure of the audio. The label, on the other hand, is a 2-dimensional array with dimensions of 7 x 258, which indicates the presence of seven distinct techniques across each time frame. indicating the existence of the seven techniques in each time frame. In the end, given that the raw dataset has already been split into train, valid, and test sets, the integrated dataset maintains the same split method. This dataset can be used for frame-level guzheng playing technique detection.
 
 ### Supported Tasks and Leaderboards
-MIR, audio classification
+MIR, audio frame-level detection, Guzheng playing technique detection
 
 ### Languages
 Chinese, English
@@ -124,33 +124,6 @@ for item in ds["test"]:
 git clone [email protected]:datasets/ccmusic-database/Guzheng_Tech99
 cd Guzheng_Tech99
 ```
-## Dataset Creation
-### Curation Rationale
-Instrument playing technique (IPT) is a key element of musical presentation.
-
-### Source Data
-#### Initial Data Collection and Normalization
-Dichucheng Li, Monan Zhou
-
-#### Who are the source language producers?
-Students from FD-LAMT
-
-### Annotations
-#### Annotation process
-Guzheng is a polyphonic instrument. In Guzheng performance, notes with different IPTs are usually overlapped and mixed IPTs that can be decomposed into multiple independent IPTs are usually used. Most existing work on IPT detection typically uses datasets with monophonic instrumental solo pieces. This dataset fills a gap in the research field.
-
-#### Who are the annotators?
-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
@@ -174,4 +147,4 @@ Dichucheng Li
 ```
 
 ### Contributions
-Promoting the development of the music AI industry
+A dataset for Guzheng playing technique frame-level detection