The cadenza woodwind dataset: Synthesised quartets for music information retrieval and machine learning.

IF 1 Q3 MULTIDISCIPLINARY SCIENCES
Data in Brief Pub Date : 2024-12-04 eCollection Date: 2024-12-01 DOI:10.1016/j.dib.2024.111199
Gerardo Roa Dabike, Trevor J Cox, Alex J Miller, Bruno M Fazenda, Simone Graetzer, Rebecca R Vos, Michael A Akeroyd, Jennifer Firth, William M Whitmer, Scott Bannister, Alinka Greasley, Jon P Barker
{"title":"The cadenza woodwind dataset: Synthesised quartets for music information retrieval and machine learning.","authors":"Gerardo Roa Dabike, Trevor J Cox, Alex J Miller, Bruno M Fazenda, Simone Graetzer, Rebecca R Vos, Michael A Akeroyd, Jennifer Firth, William M Whitmer, Scott Bannister, Alinka Greasley, Jon P Barker","doi":"10.1016/j.dib.2024.111199","DOIUrl":null,"url":null,"abstract":"<p><p>This paper presents the Cadenza Woodwind Dataset. This publicly available data is synthesised audio for woodwind quartets including renderings of each instrument in isolation. The data was created to be used as training data within Cadenza's second open machine learning challenge (CAD2) for the task on rebalancing classical music ensembles. The dataset is also intended for developing other music information retrieval (MIR) algorithms using machine learning. It was created because of the lack of large-scale datasets of classical woodwind music with separate audio for each instrument and permissive license for reuse. Music scores were selected from the OpenScore String Quartet corpus. These were rendered for two woodwind ensembles of (i) flute, oboe, clarinet and bassoon; and (ii) flute, oboe, alto saxophone and bassoon. This was done by a professional music producer using industry-standard software. Virtual instruments were used to create the audio for each instrument using software that interpreted expression markings in the score. Convolution reverberation was used to simulate a performance space and the ensembles mixed. The dataset consists of the audio and associated metadata.</p>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"57 ","pages":"111199"},"PeriodicalIF":1.0000,"publicationDate":"2024-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11683209/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Data in Brief","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1016/j.dib.2024.111199","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/12/1 0:00:00","PubModel":"eCollection","JCR":"Q3","JCRName":"MULTIDISCIPLINARY SCIENCES","Score":null,"Total":0}
引用次数: 0

Abstract

This paper presents the Cadenza Woodwind Dataset. This publicly available data is synthesised audio for woodwind quartets including renderings of each instrument in isolation. The data was created to be used as training data within Cadenza's second open machine learning challenge (CAD2) for the task on rebalancing classical music ensembles. The dataset is also intended for developing other music information retrieval (MIR) algorithms using machine learning. It was created because of the lack of large-scale datasets of classical woodwind music with separate audio for each instrument and permissive license for reuse. Music scores were selected from the OpenScore String Quartet corpus. These were rendered for two woodwind ensembles of (i) flute, oboe, clarinet and bassoon; and (ii) flute, oboe, alto saxophone and bassoon. This was done by a professional music producer using industry-standard software. Virtual instruments were used to create the audio for each instrument using software that interpreted expression markings in the score. Convolution reverberation was used to simulate a performance space and the ensembles mixed. The dataset consists of the audio and associated metadata.

华彩木管乐器数据集:音乐信息检索和机器学习的合成四重奏。
本文介绍了华彩木管乐器数据集。这些公开可用的数据是木管四重奏的合成音频,包括每个乐器的单独渲染。这些数据是为了在Cadenza的第二个开放机器学习挑战(CAD2)中用作训练数据而创建的,该挑战的任务是重新平衡古典音乐合奏。该数据集还用于使用机器学习开发其他音乐信息检索(MIR)算法。它的产生是因为缺乏大规模的古典木管音乐数据集,每种乐器都有单独的音频,并且允许重复使用。乐谱选自OpenScore弦乐四重奏语料库。这些是为两个木管乐团演奏的:(1)长笛,双簧管,单簧管和大管;(二)长笛、双簧管、中音萨克斯和巴松管。这是由专业音乐制作人使用行业标准软件完成的。虚拟乐器被用来为每个乐器创建音频,使用软件来解释乐谱中的表情标记。卷积混响被用来模拟一个表演空间和合奏混合。数据集由音频和相关元数据组成。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
Data in Brief
Data in Brief MULTIDISCIPLINARY SCIENCES-
CiteScore
3.10
自引率
0.00%
发文量
996
审稿时长
70 days
期刊介绍: Data in Brief provides a way for researchers to easily share and reuse each other''s datasets by publishing data articles that: -Thoroughly describe your data, facilitating reproducibility. -Make your data, which is often buried in supplementary material, easier to find. -Increase traffic towards associated research articles and data, leading to more citations. -Open up doors for new collaborations. Because you never know what data will be useful to someone else, Data in Brief welcomes submissions that describe data from all research areas.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信