ccmusic-database/erhu_playing_tech

Dataset Card for Erhu Playing Technique

Original Content

This dataset was created and has been utilized for Erhu playing technique detection by [1], 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.

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.

We constructed the default subset 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. 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 to supplement the evaluation of this integrated version dataset. The results of the evaluation can be viewed in [2]. 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 and 7_class subset. However, these two subsets have not been evaluated and therefore are not reflected in our paper.

Statistics

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 Structure

audio	mel	label
.wav, 44100Hz	.jpg, 44100Hz	4/7/11-class

Eval Subset Structure

mel	cqt	chroma	label
.jpg, 44100Hz	.jpg, 44100Hz	.jpg, 44100Hz	11-class

Data Instances

.zip(.wav, .jpg)

Data Fields

+ detache 分弓 (72)
  + forte (8)
  + medium (8)
  + piano (56)
+ diangong 垫弓 (28)
+ harmonic 泛音 (18)
  + natural 自然泛音 (6)
  + artificial 人工泛音 (12)
+ legato&slide&glissando 连弓&滑音&大滑音 (114)
  + glissando_down 大滑音 下行 (4)
  + glissando_up 大滑音 上行 (4)
  + huihuayin_down 下回滑音 (18)
  + huihuayin_long_down 后下回滑音 (12)
  + legato&slide_up 向上连弓 包含滑音 (24)
    + forte (8)
    + medium (8)
    + piano (8)
  + slide_dianzhi 垫指滑音 (4)
  + slide_down 向下滑音 (16)
  + slide_legato 连线滑音 (16)
  + slide_up 向上滑音 (16)
+ percussive 打击类音效 (21)
  + dajigong 大击弓 (11)
  + horse 马嘶 (2)
  + stick 敲击弓 (8)
+ pizzicato 拨弦 (96)
  + forte (30)
  + medium (29)
  + piano (30)
  + left 左手勾弦 (6)
+ ricochet 抛弓 (36)
+ staccato 顿弓 (141)
  + forte (47)
  + medium (46)
  + piano (48)
+ tremolo 颤弓 （144）
  + forte (48)
  + medium (48)
  + piano (48)
+ trill 颤音 (202)
  + long 长颤音 (141)
    + forte (46)
    + medium (47)
    + piano (48)
  + short 短颤音 (61)
    + down 下颤音 (30)
    + up 上颤音 (31)
+ vibrato 揉弦 (56)
  + late (13)
  + press 压揉 (6)
  + roll 滚揉 (28)
  + slide 滑揉 (9)

Data Splits

train, validation, test

Dataset Description

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.

Supported Tasks and Leaderboards

Erhu Playing Technique Classification

Languages

Chinese, English

Usage

Eval Subset

from datasets import load_dataset

dataset = load_dataset("ccmusic-database/erhu_playing_tech", name="eval")
for item in ds["train"]:
    print(item)

for item in ds["validation"]:
    print(item)

for item in ds["test"]:
    print(item)

4-class Subset

from datasets import load_dataset

dataset = load_dataset("ccmusic-database/erhu_playing_tech", name="4_classes")
for item in ds["train"]:
    print(item)

for item in ds["validation"]:
    print(item)

for item in ds["test"]:
    print(item)

7-class Subset

from datasets import load_dataset

ds = load_dataset("ccmusic-database/erhu_playing_tech", name="7_classes")
for item in ds["train"]:
    print(item)

for item in ds["validation"]:
    print(item)

for item in ds["test"]:
    print(item)

11-class Subset

from datasets import load_dataset
# default subset
ds = load_dataset("ccmusic-database/erhu_playing_tech", name="11_classes")
for item in ds["train"]:
    print(item)

for item in ds["validation"]:
    print(item)

for item in ds["test"]:
    print(item)

Maintenance

git clone [email protected]:datasets/ccmusic-database/erhu_playing_tech
cd erhu_playing_tech

Dataset Creation

Curation Rationale

Lack of a dataset for Erhu playing tech

Source Data

Initial Data Collection and Normalization

Zhaorui Liu, Monan Zhou

Who are the source language producers?

Students from CCMUSIC

Annotations

Annotation process

This dataset is an audio dataset containing 927 audio clips recorded by the China Conservatory of Music, each with a performance technique of erhu.

Who are the annotators?

Students from CCMUSIC

Considerations for Using the Data

Social Impact of Dataset

Advancing the Digitization Process of Traditional Chinese Instruments

Discussion of Biases

Only for Erhu

Other Known Limitations

Not Specific Enough in Categorization

Additional Information

Dataset Curators

Zijin Li

Evaluation

[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.
[2] https://huggingface.co/ccmusic-database/erhu_playing_tech

Citation Information

@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 Music Information Retrieval Research},
  month        = {mar},
  year         = {2024},
  publisher    = {HuggingFace},
  version      = {1.2},
  url          = {https://huggingface.co/ccmusic-database}
}

Contributions

Provide a dataset for Erhu playing tech