The cadenza woodwind dataset: Synthesised quartets for music information retrieval and machine learningZenodo
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...
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| Main Authors: | , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2024-12-01
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| Series: | Data in Brief |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352340924011612 |
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| author | 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 |
| author_facet | 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 |
| author_sort | Gerardo Roa Dabike |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-2e7a910c75b94d9b8d28c10c1978178d |
| institution | Kabale University |
| issn | 2352-3409 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Data in Brief |
| spelling | doaj-art-2e7a910c75b94d9b8d28c10c1978178d2024-12-11T05:56:56ZengElsevierData in Brief2352-34092024-12-0157111199The cadenza woodwind dataset: Synthesised quartets for music information retrieval and machine learningZenodoGerardo Roa Dabike0Trevor J. Cox1Alex J. Miller2Bruno M. Fazenda3Simone Graetzer4Rebecca R. Vos5Michael A. Akeroyd6Jennifer Firth7William M. Whitmer8Scott Bannister9Alinka Greasley10Jon P. Barker11Acoustics Research Centre, University of Salford, UKAcoustics Research Centre, University of Salford, UK; Corresponding author.Acoustics Research Centre, University of Salford, UKAcoustics Research Centre, University of Salford, UKAcoustics Research Centre, University of Salford, UKAcoustics Research Centre, University of Salford, UKHearing Sciences, Mental Health and Clinical Neurosciences, School of Medicine, University of Nottingham, UKHearing Sciences, Mental Health and Clinical Neurosciences, School of Medicine, University of Nottingham, UKHearing Sciences, Mental Health and Clinical Neurosciences, School of Medicine, University of Nottingham, UKSchool of Music, University of Leeds, UKSchool of Music, University of Leeds, UKDepartment of Computer Science, University of Sheffield, UKThis 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.http://www.sciencedirect.com/science/article/pii/S2352340924011612MIRAudioEnsembleDeep learning |
| spellingShingle | 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 The cadenza woodwind dataset: Synthesised quartets for music information retrieval and machine learningZenodo Data in Brief MIR Audio Ensemble Deep learning |
| title | The cadenza woodwind dataset: Synthesised quartets for music information retrieval and machine learningZenodo |
| title_full | The cadenza woodwind dataset: Synthesised quartets for music information retrieval and machine learningZenodo |
| title_fullStr | The cadenza woodwind dataset: Synthesised quartets for music information retrieval and machine learningZenodo |
| title_full_unstemmed | The cadenza woodwind dataset: Synthesised quartets for music information retrieval and machine learningZenodo |
| title_short | The cadenza woodwind dataset: Synthesised quartets for music information retrieval and machine learningZenodo |
| title_sort | cadenza woodwind dataset synthesised quartets for music information retrieval and machine learningzenodo |
| topic | MIR Audio Ensemble Deep learning |
| url | http://www.sciencedirect.com/science/article/pii/S2352340924011612 |
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