upd md

README.md

@@ -15,17 +15,37 @@ viewer: false
 ---
 
 # Dataset Card for Erhu Playing Technique
-The original dataset is sourced from [ErhuPT](https://ccmusic-database.github.io/en/database/ccm.html#shou8) and all performances are conducted by professional erhu players. These clips are categorized by annotators with proficiency in erhu performance into 11 classes, namely: split bow, pad bow, overtone, legato & glissando & slur, strike bow, plucked string, throw bow, staccato bow, tremolo, and vibrato. For certain playing techniques, multiple audio clips are available, each played at different dynamics. This dataset was created and has been utilized for erhu playing technique detection. The label system is hierarchical and contains three levels in the original dataset. The first level consists of four categories: trill, staccato, slide, and others; the second level comprises seven categories: trill/short/up, trill/long, staccato, slide up, slide/legato, slide/down, and others; the third level consists of 11 categories, representing the 11 playing techniques described earlier.
+## Original Content
+This dataset was created and has been utilized for Erhu playing technique detection by [[1]](https://arxiv.org/pdf/1910.09021), which has not undergone peer review. The original dataset comprises 1,253 Erhu audio clips, all performed by professional Erhu players. These clips were annotated according to three hierarchical levels, resulting in annotations for four, seven, and 11 categories. Part of the audio data is sourced from the CTIS dataset described earlier.
 
-After organizing the aforementioned data, we constructed the [default subset](#11-class) of the current integrated version dataset based on its 11 classification data and optimized the names of the 11 categories. The data structure can be seen in the [viewer](https://www.modelscope.cn/datasets/ccmusic-database/erhu_playing_tech/dataPeview). Although the original dataset has been cited in some articles, the experiments in those articles lack reproducibility. In order to demonstrate the effectiveness of the default subset, we further processed the data and constructed the [eval subset](#eval) to supplement the evaluation of this integrated version dataset. The results of the evaluation can be viewed in the [erhu_playing_tech](https://www.modelscope.cn/models/ccmusic-database/erhu_playing_tech). In addition, the labels of categories 4 and 7 in the original dataset were not discarded. Instead, they were separately constructed into [4_class subset](#4-class) and [7_class subset](#7-class). However, these two subsets have not been evaluated and therefore are not reflected in our paper. The following are the statistical charts for the 11_class (Default), 7_class, and 4_class subsets:
+## Integration
+We first perform label cleaning to abandon the labels for the four and seven categories, since they do not strictly form a hierarchical relationship, and there are also missing data problems. This process leaves us with only the labels for the 11 categories. Then, we add Chinese character label and Chinese pinyin label to enhance comprehensibility. The 11 labels are: Detache (分弓), Diangong (垫弓), Harmonic (泛音), Legato\slide\glissando (连弓\滑音\连音), Percussive (击弓), Pizzicato (拨弦), Ricochet (抛弓), Staccato (断弓), Tremolo (震音), Trill (颤音), and Vibrato (揉弦). After integration, the data structure contains six columns: audio (with a sampling rate of 44,100 Hz), mel spectrograms, numeric label, Italian label, Chinese character label, and Chinese pinyin label. The total number of audio clips remains at 1,253, with a total duration of 25.81 minutes. The average duration is 1.24 seconds.
 
-<img src="https://www.modelscope.cn/datasets/ccmusic-database/erhu_playing_tech/resolve/master/data/erhu.png">
+We constructed the <a href="#11-class-subset">default subset</a> of the current integrated version dataset based on its 11 classification data and optimized the names of the 11 categories. The data structure can be seen in the [viewer](https://www.modelscope.cn/datasets/ccmusic-database/erhu_playing_tech/dataPeview). Although the original dataset has been cited in some articles, the experiments in those articles lack reproducibility. In order to demonstrate the effectiveness of the default subset, we further processed the data and constructed the [eval subset](#eval-subset) to supplement the evaluation of this integrated version dataset. The results of the evaluation can be viewed in [[2]](https://huggingface.co/ccmusic-database/erhu_playing_tech). In addition, the labels of categories 4 and 7 in the original dataset were not discarded. Instead, they were separately constructed into [4_class subset](#4-class-subset) and [7_class subset](#7-class-subset). However, these two subsets have not been evaluated and therefore are not reflected in our paper.
 
-## Viewer
-<https://www.modelscope.cn/datasets/ccmusic-database/erhu_playing_tech/dataPeview>
+## Statistics
+| ![](https://www.modelscope.cn/datasets/ccmusic-database/erhu_playing_tech/resolve/master/data/erhu_pie.jpg) | ![](https://www.modelscope.cn/datasets/ccmusic-database/erhu_playing_tech/resolve/master/data/erhu.jpg) | ![](https://www.modelscope.cn/datasets/ccmusic-database/erhu_playing_tech/resolve/master/data/erhu_bar.jpg) | ![](https://www.modelscope.cn/datasets/ccmusic-database/erhu_playing_tech/resolve/master/data/erhu.png) |
+| :---------------------------------------------------------------------------------------------------------: | :-----------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------------------: | :-----------------------------------------------------------------------------------------------------: |
+|                                                 **Fig. 1**                                                  |                                               **Fig. 2**                                                |                                                 **Fig. 3**                                                  |                                               **Fig. 4**                                                |
+
+To begin with, **Fig. 1** presents the number of data entries per label. The Trill label has the highest data volume, with 249 instances, which accounts for 19.9% of the total dataset. Conversely, the Harmonic label has the least amount of data, with only 30 instances, representing a meager 2.4% of the total. Turning to the audio duration per category, as illustrated in **Fig. 2**, the audio data associated with the Trill label has the longest cumulative duration, amounting to 4.88 minutes. In contrast, the Percussive label has the shortest audio duration, clocking in at 0.75 minutes. These disparities clearly indicate a class imbalance problem within the dataset. Finally, as shown in **Fig. 3**, we count the frequency of audio occurrences at 550-ms intervals. The quantity of data decreases as the duration lengthens. The most populated duration range is 90-640 ms, with 422 audio clips. The least populated range is 3390-3940 ms, which contains only 12 clips. **Fig. 4** is the statistical charts for the 11_class (Default), 7_class, and 4_class subsets.
+
+### Totals
+|           Subset            | Total count |  Total duration(s)   |
+| :-------------------------: | :---------: | :------------------: |
+| Default / 11_classes / Eval |   `1253`    | `1548.3557823129247` |
+|    7_classes / 4_classes    |    `635`    | `719.8175736961448`  |
+
+### Range (Default subset)
+|               Statistical items                |        Values        |
+| :--------------------------------------------: | :------------------: |
+|               Mean duration(ms)                | `1235.7189004891661` |
+|                Min duration(ms)                |  `91.7687074829932`  |
+|                Max duration(ms)                | `4468.934240362812`  |
+| Classes in the longest audio duartion interval |  `Vibrato, Detache`  |
 
 ## Dataset Structure
-### Default subset
+### Default Subset Structure
 <style>
   .erhu td {
     vertical-align: middle !important;
@@ -46,14 +66,9 @@ After organizing the aforementioned data, we constructed the [default subset](#1
         <td>.jpg, 44100Hz</td>
         <td>4/7/11-class</td>
     </tr>
-    <tr>
-        <td>...</td>
-        <td>...</td>
-        <td>...</td>
-    </tr>
 </table>
 
-### Eval subset
+### Eval Subset Structure
 <table class="erhu">
     <tr>
         <th>mel</th>
@@ -67,12 +82,6 @@ After organizing the aforementioned data, we constructed the [default subset](#1
         <td>.jpg, 44100Hz</td>
         <td>11-class</td>
     </tr>
-    <tr>
-        <td>...</td>
-        <td>...</td>
-        <td>...</td>
-        <td>...</td>
-    </tr>
 </table>
 
 ### Data Instances
@@ -138,29 +147,9 @@ After organizing the aforementioned data, we constructed the [default subset](#1
 train, validation, test
 
 ## Dataset Description
-- **Homepage:** <https://ccmusic-database.github.io>
-- **Repository:** <https://huggingface.co/datasets/ccmusic-database/erhu_playing_tech>
-- **Paper:** <https://doi.org/10.5281/zenodo.5676893>
-- **Leaderboard:** <https://ccmusic-database.github.io/team.html>
-- **Point of Contact:** <https://www.modelscope.cn/datasets/ccmusic-database/erhu_playing_tech>
-
 ### Dataset Summary
 The label system is hierarchical and contains three levels in the raw dataset. The first level consists of four categories: _trill, staccato, slide_, and _others_; the second level comprises seven categories: _trill\short\up, trill\long, staccato, slide up, slide\legato, slide\down_, and _others_; the third level consists of 11 categories, representing the 11 playing techniques described earlier. Although it also employs a three-level label system, the higher-level labels do not exhibit complete downward compatibility with the lower-level labels. Therefore, we cannot merge these three-level labels into the same split but must treat them as three separate subsets.
 
-#### Totals 总量统计
-|         Subset 子集         | Total count 总数据量 | Total duration(s) 总时长(秒) |
-| :-------------------------: | :------------------: | :--------------------------: |
-| Default / 11_classes / Eval |        `1253`        |     `1548.3557823129247`     |
-|    7_classes / 4_classes    |        `635`         |     `719.8175736961448`      |
-
-#### Range (Default subset) 默认子集极值
-|      Statistical items 统计项      |      Values 值       |
-| :--------------------------------: | :------------------: |
-|  Mean duration(ms) 平均时长(毫秒)  | `1235.7189004891661` |
-|  Min duration(ms) 最短时长(毫秒)   |  `91.7687074829932`  |
-|  Max duration(ms) 最长时长(毫秒)   | `4468.934240362812`  |
-| Classes with max durs 最长时长类别 |  `Vibrato, Detache`  |
-
 ### Supported Tasks and Leaderboards
 Erhu Playing Technique Classification
 
@@ -168,7 +157,7 @@ Erhu Playing Technique Classification
 Chinese, English
 
 ## Usage
-### Eval
+### Eval Subset
 ```python
 from datasets import load_dataset
 
@@ -183,7 +172,7 @@ for item in ds["test"]:
     print(item)
 ```
 
-### 4-class
+### 4-class Subset
 ```python
 from datasets import load_dataset
 
@@ -198,7 +187,7 @@ for item in ds["test"]:
     print(item)
 ```
 
-### 7-class
+### 7-class Subset
 ```python
 from datasets import load_dataset
 
@@ -213,7 +202,7 @@ for item in ds["test"]:
     print(item)
 ```
 
-### 11-class
+### 11-class Subset
 ```python
 from datasets import load_dataset
 # default
@@ -252,9 +241,6 @@ This dataset is an audio dataset containing 927 audio clips recorded by the Chin
 #### Who are the annotators?
 Students from CCMUSIC
 
-### Personal and Sensitive Information
-None
-
 ## Considerations for Using the Data
 ### Social Impact of Dataset
 Advancing the Digitization Process of Traditional Chinese Instruments
@@ -270,13 +256,14 @@ Not Specific Enough in Categorization
 Zijin Li
 
 ### Evaluation
-[Wang, Zehao et al. “Musical Instrument Playing Technique Detection Based on FCN: Using Chinese Bowed-Stringed Instrument as an Example.” ArXiv abs/1910.09021 (2019): n. pag.](https://arxiv.org/pdf/1910.09021.pdf)
+[1] [Wang, Zehao et al. “Musical Instrument Playing Technique Detection Based on FCN: Using Chinese Bowed-Stringed Instrument as an Example.” ArXiv abs/1910.09021 (2019): n. pag.](https://arxiv.org/pdf/1910.09021.pdf)<br>
+[2] <https://huggingface.co/ccmusic-database/erhu_playing_tech>
 
 ### Citation Information
 ```bibtex
 @dataset{zhaorui_liu_2021_5676893,
   author       = {Monan Zhou, Shenyang Xu, Zhaorui Liu, Zhaowen Wang, Feng Yu, Wei Li and Baoqiang Han},
-  title        = {CCMusic: an Open and Diverse Database for Chinese and General Music Information Retrieval Research},
+  title        = {CCMusic: an Open and Diverse Database for Chinese Music Information Retrieval Research},
   month        = {mar},
   year         = {2024},
   publisher    = {HuggingFace},