cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.1432913
title: OpenMIC-2018

description: The OpenMIC-2018 dataset is made available through a collaboration between Spotify and MARL@NYU. Additionally, the cost of annotation was sponsored by Spotify, whose contributions to open-source research can be found online at the developer site, engineering blog, and public GitHub. If you use this dataset, please cite the following work: Humphrey, Eric J., Durand, Simon, and McFee, Brian. "OpenMIC-2018: An Open Dataset for Multiple Instrument Recognition." in Proceedings of the 19th International Society for Music Information Retrieval Conference (ISMIR), 2018. [pdf] The dataset is made available by Spotify AB under a Creative Commons Attribution 4.0 International (CC BY 4.0) license. The full terms of this license are included alongside this dataset. This dataset contains the following: 10 second snippets of audio, in a directory format like 'audio/{0:3}/{0}.ogg'.format(sample_key) VGGish features as JSON objects, in a directory format like 'vggish/{0:3}/{0}.json'.format(sample_key) MD5 checksums for each OGG and JSON file Anonymized individual responses, in 'openmic-2018-individual-responses.csv' Aggregated labels, in 'openmic-2018-aggregated-labels.csv' Track metadata, with licenses for each audio recording, in 'openmic-2018-metadata.csv' A Python-friendly NPZ file of features and labels, 'openmic-2018.npz' Sample partitions for train and test, in 'partitions/*.txt'
description: {"references": ["Humphrey, Eric J., Durand, Simon, and McFee, Brian. \"OpenMIC-2018: An Open Dataset for Multiple Instrument Recognition.\" in Proceedings of the 19th International Society for Music Information Retrieval Conference (ISMIR), 2018."]}
DOI: 10.5281/zenodo.1432912
title: OpenMIC-2018

description: The OpenMIC-2018 dataset is made available through a collaboration between Spotify and MARL@NYU. Additionally, the cost of annotation was sponsored by Spotify, whose contributions to open-source research can be found online at the developer site, engineering blog, and public GitHub. If you use this dataset, please cite the following work: Humphrey, Eric J., Durand, Simon, and McFee, Brian. "OpenMIC-2018: An Open Dataset for Multiple Instrument Recognition." in Proceedings of the 19th International Society for Music Information Retrieval Conference (ISMIR), 2018. [pdf] The dataset is made available by Spotify AB under a Creative Commons Attribution 4.0 International (CC BY 4.0) license. The full terms of this license are included alongside this dataset. This dataset contains the following: 10 second snippets of audio, in a directory format like 'audio/{0:3}/{0}.ogg'.format(sample_key) VGGish features as JSON objects, in a directory format like 'vggish/{0:3}/{0}.json'.format(sample_key) MD5 checksums for each OGG and JSON file Anonymized individual responses, in 'openmic-2018-individual-responses.csv' Aggregated labels, in 'openmic-2018-aggregated-labels.csv' Track metadata, with licenses for each audio recording, in 'openmic-2018-metadata.csv' A Python-friendly NPZ file of features and labels, 'openmic-2018.npz' Sample partitions for train and test, in 'partitions/*.txt'
description: {"references": ["Humphrey, Eric J., Durand, Simon, and McFee, Brian. \"OpenMIC-2018: An Open Dataset for Multiple Instrument Recognition.\" in Proceedings of the 19th International Society for Music Information Retrieval Conference (ISMIR), 2018."]}
DOI: 10.5281/zenodo.1341071
title: Jingju a cappella singing voice test dataset for "An efficient deep learning model for musical onset detection"

description: Jingju a cappella singing voice test dataset used in the paper "An efficient deep learning model for musical onset detection". Arxiv paper link: https://arxiv.org/abs/1806.06773 Supplementary information and code for the paper: https://github.com/ronggong/musical-onset-efficient <strong>Content:</strong> ismir_2018_dataset_for_reviewing.zip: audio, syllable boundary and label annotation jingju dataset train test split filenames.xlsx: train and test split filename list <strong>Citation:</strong> <pre>@article{gong2018towards, title={Towards an efficient deep learning model for musical onset detection}, author={Gong, Rong and Serra, Xavier}, journal={arXiv preprint arXiv:1806.06773}, year={2018} } </pre> <strong>Contact:</strong> Rong Gong: rong.gong&lt;at&gt;upf.edu
DOI: 10.5281/zenodo.1341070
title: Jingju a cappella singing voice test dataset for "An efficient deep learning model for musical onset detection"

description: Jingju a cappella singing voice test dataset used in the paper "An efficient deep learning model for musical onset detection". Arxiv paper link: https://arxiv.org/abs/1806.06773 Supplementary information and code for the paper: https://github.com/ronggong/musical-onset-efficient <strong>Content:</strong> ismir_2018_dataset_for_reviewing.zip: audio, syllable boundary and label annotation jingju dataset train test split filenames.xlsx: train and test split filename list <strong>Citation:</strong> <pre>@article{gong2018towards, title={Towards an efficient deep learning model for musical onset detection}, author={Gong, Rong and Serra, Xavier}, journal={arXiv preprint arXiv:1806.06773}, year={2018} } </pre> <strong>Contact:</strong> Rong Gong: rong.gong&lt;at&gt;upf.edu
DOI: 10.5281/zenodo.1286350
title: Jingju a cappella singing dataset part3

description: 这是京剧清唱数据库的第三部分。这个部分集中于音乐教育的应用。对每个唱段,我们采集了老师和学生的录音。 This is the 3rd part of the jingju a cappella singing dataset. This part focus on the music education application. For this purpose, we collected both "teacher" and "students" recordings for each aria. <strong>文件 Files:</strong> wav_left.zip: audio files in .wav format, mono textgrid.zip: line, syllable and phoneme time boundaries and labels, in Praat .textgrid format annotation_txt.zip: line, syllable and phoneme time boundaries and labels, in .txt format *phrase_char: phrase-level time boundaries, labeled in Mandarin characters *phrase: phrase-level time boundaries, labeled in Mandarin pinyin *syllable: syllable-level time boundaries, labeled in Mandarin pinyin *phoneme: phoneme-level time boundaries, labeled in X-SAMPA arias.ods: spreadsheet containing the detailed information of each recording -- role-type, shengqiang, banshi, singer and melodic line, syllable, phoneme numbers. <strong>艺术家 Artists:</strong> 示范录音取自3位年轻的专业京剧演员。学生录音取自几位非专业小学生和非艺术类大学的学生。 Teacher recordings are recorded by 3 professional young jingju performers. Student recordings are performed by several non-jingju professional primary school students and some non-art university students. <strong>标注 Annotation:</strong> 数据库包含一部分录音的唱句起始位置和音节起始位置标注,标注格式为Praat TextGrid。唱句标注包含有每一唱句的歌词,此歌词从曲谱提取,并不与实际演唱一致;音节标注包含拼音,音素标注为X-SAMPA,经过作者修正,试图与演唱发音一致。 The dataset contains the line and syllable boundary annotation for a part of recordings, in Praat TextGrid format. The line annotation contains the lyrics for each line, which is extracted from the score, and might not coherent with the actual singing; the syllable annotation contains pinyin, phoneme annotation uses X-SAMPA, corrected by the author to be coherent with the actual singing. 标注X-SAMPA格式和其他信息可以参考以下链接: Annotation format, units, parsing code and other information please refer to: https://github.com/MTG/jingjuPhonemeAnnotation <strong>协议 License:</strong> Creative Commons Attribution-NonCommercial 4.0 <strong>联系方式 Contact information</strong>: <em>如果任何问题,请联系作者 </em><em>If you have any question, please contact the author:</em> 龚嵘 Rong Gong: Email - rong&lt;dot&gt;gong&lt;at&gt;upf&lt;dot&gt;edu, Wechat id - gongr86
DOI: 10.5281/zenodo.1244732
title: Jingju a cappella singing dataset part3

description: 这是京剧清唱数据库的第三部分。这个部分集中于音乐教育的应用。对每个唱段,我们采集了老师和学生的录音。 This is the 3rd part of the jingju a cappella singing dataset. This part focus on the music education application. For this purpose, we collected both "teacher" and "students" recordings for each aria. <strong>文件 Files:</strong> wav_left.zip: audio files in .wav format, mono textgrid.zip: line, syllable and phoneme time boundaries and labels, in Praat .textgrid format annotation_txt.zip: line, syllable and phoneme time boundaries and labels, in .txt format *phrase_char: phrase-level time boundaries, labeled in Mandarin characters *phrase: phrase-level time boundaries, labeled in Mandarin pinyin *syllable: syllable-level time boundaries, labeled in Mandarin pinyin *phoneme: phoneme-level time boundaries, labeled in X-SAMPA arias.ods: spreadsheet containing the detailed information of each recording -- role-type, shengqiang, banshi, singer and melodic line, syllable, phoneme numbers. <strong>艺术家 Artists:</strong> 示范录音取自3位年轻的专业京剧演员。学生录音取自几位非专业小学生和非艺术类大学的学生。 Teacher recordings are recorded by 3 professional young jingju performers. Student recordings are performed by several non-jingju professional primary school students and some non-art university students. <strong>标注 Annotation:</strong> 数据库包含一部分录音的唱句起始位置和音节起始位置标注,标注格式为Praat TextGrid。唱句标注包含有每一唱句的歌词,此歌词从曲谱提取,并不与实际演唱一致;音节标注包含拼音,音素标注为X-SAMPA,经过作者修正,试图与演唱发音一致。 The dataset contains the line and syllable boundary annotation for a part of recordings, in Praat TextGrid format. The line annotation contains the lyrics for each line, which is extracted from the score, and might not coherent with the actual singing; the syllable annotation contains pinyin, phoneme annotation uses X-SAMPA, corrected by the author to be coherent with the actual singing. 标注X-SAMPA格式和其他信息可以参考以下链接: Annotation format, units, parsing code and other information please refer to: https://github.com/MTG/jingjuPhonemeAnnotation <strong>协议 License:</strong> Creative Commons Attribution-NonCommercial 4.0 <strong>联系方式 Contact information</strong>: <em>如果任何问题,请联系作者 </em><em>If you have any question, please contact the author:</em> 龚嵘 Rong Gong: Email - rong&lt;dot&gt;gong&lt;at&gt;upf&lt;dot&gt;edu, Wechat id - gongr86
DOI: 10.5281/zenodo.1185124
title: Dataset for Interspeech 2018 submission: Singing voice phoneme segmentation by hierarchically inferring syllable and phoneme onset positions

description: This dataset contains the materials for training, testing the joint and HSMM models mentioned in the paper "<em>Singing voice phoneme segmentation by hierarchically inferring syllable and phoneme onset positions"</em>. The filename list of this dataset can be found in the function <em>get_train_test_recordings_joint()</em> of <em>./general/trainTestSeparation.py</em> file. The dataset contains the Praat TextGrids and .wavs of the variables: <em>train_primary_school, val_primary_school</em> and <em>test_primary_school</em>. For accessing other datasets such as <em>train_nacta_2017, train_nacta</em> and <em>train_sepa</em>, please download them from the links: jingju dataset part1: https://zenodo.org/record/1185154 jingju dataset part2: https://doi.org/10.5281/zenodo.842229 Once you have downloaded these three datasets, you need to set the paths in <em>./general/filePathShared.py</em>. Set <em>path_jingju_dataset</em> to the parent path of these three datasets. Set <em>primarySchool_dataset_root_path</em> to the path of the interspeech2018 dataset (the current dataset). Set <em>nacta_dataset_root_path</em> to the path of the jingju dataset part1. Set <em>nacta2017_dataset_root_path</em> to the path the jingju dataset part2. For more information on this paper, please refer to the Github page: https://github.com/ronggong/interspeech2018_submission01
DOI: 10.5281/zenodo.1185123
title: Dataset for Interspeech 2018 submission: Singing voice phoneme segmentation by hierarchically inferring syllable and phoneme onset positions

description: This dataset contains the materials for training, testing the joint and HSMM models mentioned in the paper "<em>Singing voice phoneme segmentation by hierarchically inferring syllable and phoneme onset positions"</em>. The filename list of this dataset can be found in the function <em>get_train_test_recordings_joint()</em> of <em>./general/trainTestSeparation.py</em> file. The dataset contains the Praat TextGrids and .wavs of the variables: <em>train_primary_school, val_primary_school</em> and <em>test_primary_school</em>. For accessing other datasets such as <em>train_nacta_2017, train_nacta</em> and <em>train_sepa</em>, please download them from the links: jingju dataset part1: https://zenodo.org/record/1185154 jingju dataset part2: https://doi.org/10.5281/zenodo.842229 Once you have downloaded these three datasets, you need to set the paths in <em>./general/filePathShared.py</em>. Set <em>path_jingju_dataset</em> to the parent path of these three datasets. Set <em>primarySchool_dataset_root_path</em> to the path of the interspeech2018 dataset (the current dataset). Set <em>nacta_dataset_root_path</em> to the path of the jingju dataset part1. Set <em>nacta2017_dataset_root_path</em> to the path the jingju dataset part2. For more information on this paper, please refer to the Github page: https://github.com/ronggong/interspeech2018_submission01
DOI: 10.5281/zenodo.1101082
title: Beatport EDM Key Dataset

description: <em>The Beatport EDM Key Dataset</em> includes 1,486 two-minute sound excerpts from various EDM subgenres, annotated with single-key labels, comments and confidence levels generously provided by Eduard Mas Marín, and thoroughly revised and expanded by Ángel Faraldo. The original audio samples belong to online audio snippets from Beatport, an online music store for DJ's and Electronic Dance Music Producers (&lt;http:\\www.beatport.com&gt;). If this dataset were used in further research, we would appreciate the citation of the current DOI (10.5281/zenodo.1101082) and the following doctoral dissertation, where a detailed description of the properties of this dataset can be found: Ángel Faraldo (2017). <em>Tonality Estimation in Electronic Dance Music: A Computational and Musically Informed Examination.</em> PhD Thesis. Universitat Pompeu Fabra, Barcelona. This dataset is mainly intended to assess the performance of computational key estimation algorithms in electronic dance music subgenres.
DOI: 10.5281/zenodo.1101081
title: Beatport EDM Key Dataset

description: <em>The Beatport EDM Key Dataset</em> includes 1,486 two-minute sound excerpts from various EDM subgenres, annotated with single-key labels, comments and confidence levels generously provided by Eduard Mas Marín, and thoroughly revised and expanded by Ángel Faraldo. The original audio samples belong to online audio snippets from Beatport, an online music store for DJ's and Electronic Dance Music Producers (&lt;http:\\www.beatport.com&gt;). If this dataset were used in further research, we would appreciate the citation of the current DOI (10.5281/zenodo.1101082) and the following doctoral dissertation, where a detailed description of the properties of this dataset can be found: Ángel Faraldo (2017). <em>Tonality Estimation in Electronic Dance Music: A Computational and Musically Informed Examination.</em> PhD Thesis. Universitat Pompeu Fabra, Barcelona. This dataset is mainly intended to assess the performance of computational key estimation algorithms in electronic dance music subgenres.
DOI: 10.5281/zenodo.3961560
title: User-aware music auto-tagging with contextual tags

description: This is a user-aware music dataset labeled with the contextual use of each track according to each user. The dataset is composed of 10 contextual tags extracted based on user's usage through created playlists in the Deezer catalog. The tags are: " car, gym, happy, night, relax, running, sad, summer, work, workout". For each track/user pair, a contextual tag is associated with it indicating that the user listens to the track in the associated context. Additionally, the users are represented as embeddings based on their listening history computed through the matrix factorization of the user/track matrix. The creation of the dataset and the baseline of our auto-tagging model is described in the paper: Karim M. Ibrahim, Elena V. Epure, Geoffroy Peeters, and Gaël Richard. "Should we consider the users in contextual music auto-tagging models?" <em>21st International Society for Music Information Retrieval Conference (ISMIR)</em>. 2020. The source code of the paper is available here: https://github.com/KarimMibrahim/user-aware-music-autotagging The dataset is composed of the SONG_ID which is the ID of the track in the Deezer catalog. Each track/user pair is labeled with each tag as either 1 (indicating a track's presence in the context) or 0 (indicating a track's absence). The 30 seconds track previews used to train the model in the paper can be accessed through the Deezer API: https://developers.deezer.com/api. Each user is represented with an anonymized USER_ID which is associated with the user embedding available in the user_embeddings.csv file.
DOI: 10.5281/zenodo.3961559
title: User-aware music auto-tagging with contextual tags

description: This is a user-aware music dataset labeled with the contextual use of each track according to each user. The dataset is composed of 10 contextual tags extracted based on user's usage through created playlists in the Deezer catalog. The tags are: " car, gym, happy, night, relax, running, sad, summer, work, workout". For each track/user pair, a contextual tag is associated with it indicating that the user listens to the track in the associated context. Additionally, the users are represented as embeddings based on their listening history computed through the matrix factorization of the user/track matrix. The creation of the dataset and the baseline of our auto-tagging model is described in the paper: Karim M. Ibrahim, Elena V. Epure, Geoffroy Peeters, and Gaël Richard. "Should we consider the users in contextual music auto-tagging models?" <em>21st International Society for Music Information Retrieval Conference (ISMIR)</em>. 2020. The source code of the paper is available here: https://github.com/KarimMibrahim/user-aware-music-autotagging The dataset is composed of the SONG_ID which is the ID of the track in the Deezer catalog. Each track/user pair is labeled with each tag as either 1 (indicating a track's presence in the context) or 0 (indicating a track's absence). The 30 seconds track previews used to train the model in the paper can be accessed through the Deezer API: https://developers.deezer.com/api. Each user is represented with an anonymized USER_ID which is associated with the user embedding available in the user_embeddings.csv file.
DOI: 10.5281/zenodo.1014026
title: Correction occurrence analysis spreadsheet

description: The accompanying "Correction occurrence" spreadsheet for the paper: 这个文件-“纠正事件”Excel表格是下面论文的附属文件: Identification of potential Music Information Retrieval technologies for computer-aided jingju singing training, Rong Gong, Xavier Serra, Chinese traditional music technology session - China conference on sound and music technology 2017, Suzhou, China Each occurrence is annotated with (1) teacher's feedback (2) signal analysis method (3) classified dimension or detailed elements. 每一个纠正事件都标注有(1)老师的反馈,(2)所使用的信号分析方法,(3)分类的维度和细节元素。
DOI: 10.5281/zenodo.1014025
title: Correction occurrence analysis spreadsheet

description: The accompanying "Correction occurrence" spreadsheet for the paper: 这个文件-“纠正事件”Excel表格是下面论文的附属文件: Identification of potential Music Information Retrieval technologies for computer-aided jingju singing training, Rong Gong, Xavier Serra, Chinese traditional music technology session - China conference on sound and music technology 2017, Suzhou, China Each occurrence is annotated with (1) teacher's feedback (2) signal analysis method (3) classified dimension or detailed elements. 每一个纠正事件都标注有(1)老师的反馈,(2)所使用的信号分析方法,(3)分类的维度和细节元素。
DOI: 10.5281/zenodo.832736
title: Jingju a cappella singing pitch contour segmentation ground truth dataset

description: The dataset used in the paper: Gong, Rong; Yang, Yile; Serra, Xavier; Pitch Contour Segmentation for Computer-aided Jingju Singing Training Sound and Music Computing (SMC 2016), 2016, Hamburg, Germany is in "dataset" folder. The a cappella singing audio recordings are not contained in this folder due to their large size, please contact the paper authors to request them (rong.gong@upf.edu). In the "dataset" folder you can find: ground truth Jinging singing scores in .xml format used for estimating the bigram note transition probabilities. The ground truth annotation is used for: melodic transcription (male_12_pos_1 missing) parameter optimization, evaluating the StdCdLe thresholding and the overall segmentation performance. The subfolder "groundtruth" contains the following annotation for each jingju a cappella audio: file name: description (format) *_melodicTrans.csv: melodic transcription ground truth used for the evaluation (start_time pitch duration -). *_coarseSeg.csv: StdCdLe ground truth used for the parameter optimization and the evaluation (segmentation points). *_refinedSeg.csv: ground truth used for optimizing other parameters and the evaluation (start_time - duration). *_pitchtrack.csv: pitch track (contour) extracted by pYIN pitch-tracking algorithm (filename time pitch). *_monoNoteOut.csv: notes estimated by pYIN note-tracking algorithm (filename start_time duration pitch).
description: {"references": ["Gong, Rong et al. (2016). Pitch Contour Segmentation for Computer-aided Jingju Singing Training. Hamburg, Germany."]}
DOI: 10.5281/zenodo.579603
title: The MIDI Linked Data Cloud dataset

description: The study of music is highly interdisciplinary, and thus requires the combination of datasets from multiple musical domains, such as catalog metadata (authors, song titles, dates), industrial records (labels, producers, sales), and music notation (scores). While today an abundance of music metadata exists on the Linked Open Data cloud, datasets containing interoperable symbolic descriptions of music itself, i.e. music notation with note and instrument level information, are scarce. This is the MIDI Linked Data Cloud, a dataset that represents multiple collections of digital music in the MIDI standard format as Linked Data. At the time of writing, the dataset comprises 10,215,557,355 triples of 308,443 interconnected MIDI scores, and provides Web-compatible descriptions of their MIDI events.
description: {"references": ["Mero\u00f1o-Pe\u00f1uela, A. et al. (2017). The MIDI Linked Data Cloud. doi:10.5281/zenodo.579603"]}
DOI: 10.5281/zenodo.832735
title: Jingju a cappella singing pitch contour segmentation ground truth dataset

description: The dataset used in the paper: Gong, Rong; Yang, Yile; Serra, Xavier; Pitch Contour Segmentation for Computer-aided Jingju Singing Training Sound and Music Computing (SMC 2016), 2016, Hamburg, Germany is in "dataset" folder. The a cappella singing audio recordings are not contained in this folder due to their large size, please contact the paper authors to request them (rong.gong@upf.edu). In the "dataset" folder you can find: ground truth Jinging singing scores in .xml format used for estimating the bigram note transition probabilities. The ground truth annotation is used for: melodic transcription (male_12_pos_1 missing) parameter optimization, evaluating the StdCdLe thresholding and the overall segmentation performance. The subfolder "groundtruth" contains the following annotation for each jingju a cappella audio: file name: description (format) *_melodicTrans.csv: melodic transcription ground truth used for the evaluation (start_time pitch duration -). *_coarseSeg.csv: StdCdLe ground truth used for the parameter optimization and the evaluation (segmentation points). *_refinedSeg.csv: ground truth used for optimizing other parameters and the evaluation (start_time - duration). *_pitchtrack.csv: pitch track (contour) extracted by pYIN pitch-tracking algorithm (filename time pitch). *_monoNoteOut.csv: notes estimated by pYIN note-tracking algorithm (filename start_time duration pitch).
description: {"references": ["Gong, Rong et al. (2016). Pitch Contour Segmentation for Computer-aided Jingju Singing Training. Hamburg, Germany."]}