--- title: instrument_timbre canonical_url: "https://www.modelscope.cn/datasets/ccmusic-database/instrument_timbre" md_url: "https://www.modelscope.cn/datasets/ccmusic-database/instrument_timbre.md" repository: ccmusic-database/instrument_timbre chinese_name: "乐器音色打分数据集 Instruments Timbre Evaluation" last_updated: 2026-08-05 license: CC-BY-NC-ND storage_size: "102 MB" domain: - audio tasks: - audio-classification downloads: 1872 stars: 16 --- # instrument_timbre > instrument_timbre - ccmusic-database 在 ModelScope 开源的数据集。本数据集是用于对37种民族乐器的音色主观评价实验，包含用作音色主观评价实验的汇总音频素材1个（.wav格式），以及37种乐器在16个音色评价词上的音色主观评价实验（1~10分）打分表（.xlsx格式）。此外还有对10种乐器的频谱分析报告10个（.docx格式），乐器音频来自 中国传统乐器音响数据库（CTIS）。 ccmusic-database/instrument_timbre 是 ModelScope 魔搭社区上的audio-classification数据集，涉及 audio 领域，存储大小 102 MB，采用 CC-BY-NC-ND 许可。 - **Repository**: ccmusic-database/instrument_timbre - **License**: CC-BY-NC-ND - **Tasks**: audio-classification - **Domain**: audio - **Storage size**: 102 MB - **Downloads**: 1872 - **Stars**: 16 - **Last updated**: 2026-08-05 Source: https://www.modelscope.cn/datasets/ccmusic-database/instrument_timbre --- # Intro 简介 The original dataset is sourced from the [National Musical Instruments Timbre Evaluation Dataset](https://ccmusic-database.github.io/en/database/ccm.html#shou4), which includes subjective timbre evaluation scores using 16 terms such as bright, dark, raspy, etc., evaluated across 37 Chinese instruments and 24 Western instruments by participants with musical backgrounds in a subjective evaluation experiment. Additionally, it contains 10 spectrogram analysis reports for 10 instruments. Based on the aforementioned original dataset, after data processing, we have constructed the [default subset](#usage-快速使用) of the current integrated version of the dataset, dividing the Chinese section and the Western section into two splits. Each split consists of multiple data entries, with each entry structured across 18 columns. The Chinese split includes 37 entries, while the Western split comprises 24 entries. The first column of each data entry presents the instrument recordings in .wav format, sampled at a rate of 44,100 Hz. The second column provides the Chinese pinyin or English name of the instrument. The following 16 columns correspond to the 9-point scores of the 16 terms. This dataset is suitable for conducting timbre analysis of musical instruments and can also be utilized for various single or multiple regression tasks related to term scoring. The data structure of the default subset can be viewed in the [viewer](https://www.modelscope.cn/datasets/ccmusic-database/instrument_timbre/dataPeview). 原始数据集来源于 [民族乐器音色评价数据集](https://ccmusic-database.github.io/database/ccm.html#shou4), 包含了由具有音乐背景的参与者在一个主观评价实验中对 37 件中国乐器和 24 件西方乐器进行的主观音色评价得分, 评价术语包括明亮、暗淡、干瘪等 16 个词汇。此外, 数据集还包括 10 件乐器的频谱分析报告。 基于上述原始数据, 经过数据处理, 我们构建了当前集成版数据集的 [默认子集](#usage-快速使用), 我们将中国乐器部分和西方乐器部分划分为两个分割。每个分割由多个数据条目组成, 每个条目都跨越 18 列。中国乐器分割包括37个条目, 而西方乐器分割包括 24 个条目。每个数据条目的首列展示了采样率为 44,100Hz 的 .wav 格式的乐器录音。第二列提供了乐器的汉语拼音或英文名称。接下来的 16 列对应于 16 个术语的 9 分制得分。该数据集适用于进行乐器的音色分析, 也可以用于与术语得分相关的各种单变量或多变量回归任务。默认子集的数据结构可以在 [数据预览](https://www.modelscope.cn/datasets/ccmusic-database/instrument_timbre/dataPeview) 中查看。由于当前数据集已被两篇文章引用和使用, 因此省略了进一步构建校验子集的步骤。 ## Totals 总量统计 | Split | Chinese | Western | Total | | :---------: | :------: | :-----------------: | :-----------------: | | Count | `37` | `24` | `61` | | Duration(s) | `240.36` | `403.4965235260771` | `643.8565235260771` | ## Usage 快速使用 :modelscope-code[]{type="sdk"} ## Labels 标签 | Instrument 乐器名称 | Label 标签编号 | Type 乐器类型 | | :-----------------: | :------------: | :------------: | | gao_hu | 0 | Chinese | | er_hu | 1 | Chinese | | zhong_hu | 2 | Chinese | | ge_hu | 3 | Chinese | | di_yin_ge_hu | 4 | Chinese | | jing_hu | 5 | Chinese | | ban_hu | 6 | Chinese | | bang_di | 7 | Chinese | | qu_di | 8 | Chinese | | xin_di | 9 | Chinese | | da_di | 10 | Chinese | | gao_yin_sheng | 11 | Chinese | | zhong_yin_sheng | 12 | Chinese | | di_yin_sheng | 13 | Chinese | | gao_yin_suo_na | 14 | Chinese | | zhong_yin_suo_na | 15 | Chinese | | ci_zhong_yin_suo_na | 16 | Chinese | | di_yin_suo_na | 17 | Chinese | | gao_yin_guan | 18 | Chinese | | zhong_yin_guan | 19 | Chinese | | di_yin_guan | 20 | Chinese | | bei_di_yin_guan | 21 | Chinese | | ba_wu | 22 | Chinese | | xun | 23 | Chinese | | xiao | 24 | Chinese | | liu_qin | 25 | Chinese | | xiao_ruan | 26 | Chinese | | pi_pa | 27 | Chinese | | yang_qin | 28 | Chinese | | zhong_ruan | 29 | Chinese | | da_ruan | 30 | Chinese | | gu_zheng | 31 | Chinese | | gu_qin | 32 | Chinese | | kong_hou | 33 | Chinese | | san_xian | 34 | Chinese | | yun_luo | 35 | Chinese | | bian_zhong | 36 | Chinese | | violin | 37 | Western | | viola | 38 | Western | | cello | 39 | Western | | double_bass | 40 | Western | | piccolo | 41 | Western | | flute | 42 | Western | | oboe | 43 | Western | | clarinet | 44 | Western | | bassoon | 45 | Western | | saxophone | 46 | Western | | trumpet | 47 | Western | | trombone | 48 | Western | | horn | 49 | Western | | tuba | 50 | Western | | harp | 51 | Western | | tubular_bells | 52 | Western | | bells | 53 | Western | | xylophone | 54 | Western | | vibraphone | 55 | Western | | marimba | 56 | Western | | piano | 57 | Western | | clavichord | 58 | Western | | accordion | 59 | Western | | organ | 60 | Western | ## Scores 指标 | Score 指标 | Translation 翻译 | | :--------: | :--------------: | | Dark | 暗淡 | | Sharp | 尖锐 | | Consonant | 协和 | | Pure | 纯净 | | Coarse | 粗糙 | | Silvery | 清脆 | | Slim | 纤细 | | Thin | 单薄 | | Full | 丰满 | | Muddy | 混浊 | | Mellow | 柔和 | | Raspy | 干瘪 | | Thick | 厚实 | | Bright | 明亮 | | Hoarse | 嘶哑 | | Vigorous | 浑厚 | ## Clone with HTTP 下载 ```bash GIT_LFS_SKIP_SMUDGE=1 ``` :modelscope-code[]{type="git"} ## Mirror 镜像 ## Evaluation 校验 [1] [Jiang W, Liu J, Zhang X, Wang S, Jiang Y. Analysis and Modeling of Timbre Perception Features in Musical Sounds. Applied Sciences. 2020; 10(3):789.](https://www.mdpi.com/2076-3417/10/3/789) ## Cite 引用 ```bibtex @article{Jiang2020AnalysisAM, title = {Analysis and Modeling of Timbre Perception Features in Musical Sounds}, author = {Wei Jiang and Jingyu Liu and Xiaoyi Zhang and Shuang Wang and Yujian Jiang}, journal = {Applied Sciences}, year = {2020}, url = {https://api.semanticscholar.org/CorpusID:210878781} } ```