Automatic Classification of Musical Instrument Samples
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| Název: | Automatic Classification of Musical Instrument Samples |
|---|---|
| Autoři: | Daniele Scarano |
| Informace o vydavateli: | Zenodo |
| Rok vydání: | 2016 |
| Sbírka: | Zenodo |
| Témata: | machine learning, signal processing, Freesound, content based classification |
| Popis: | Automatic classification of musical instrument is an old research topic in music information retrieval. In this work we address the problem of the classification using musical instrument single note samples from Freesound and we put the accent on the content analysis of the sound and how those content information are connected to the physical characteristics of each instrument. We build a taxonomy based on instrument families and mode of excitation. The musical instruments play a central role in this work and the studies on timbre are used as a methodological base to apply feature selection to our complete set of descriptors, the aim of this is to find which descriptors are relevant to describe a specific instrument. The machine learning then is used as an instrument to evaluate our choices, to identify weakness and problem in the current implementation of audio descriptors. |
| Druh dokumentu: | text |
| Jazyk: | English |
| Relation: | https://zenodo.org/communities/smc-master/; https://zenodo.org/records/3786212; oai:zenodo.org:3786212; https://doi.org/10.5281/zenodo.3786212 |
| DOI: | 10.5281/zenodo.3786212 |
| Dostupnost: | https://doi.org/10.5281/zenodo.3786212 https://zenodo.org/records/3786212 |
| Rights: | Creative Commons Attribution 4.0 International ; cc-by-4.0 ; https://creativecommons.org/licenses/by/4.0/legalcode |
| Přístupové číslo: | edsbas.BDD3BA2E |
| Databáze: | BASE |
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| Items | – Name: Title Label: Title Group: Ti Data: Automatic Classification of Musical Instrument Samples – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Daniele+Scarano%22">Daniele Scarano</searchLink> – Name: Publisher Label: Publisher Information Group: PubInfo Data: Zenodo – Name: DatePubCY Label: Publication Year Group: Date Data: 2016 – Name: Subset Label: Collection Group: HoldingsInfo Data: Zenodo – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22machine+learning%22">machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22signal+processing%22">signal processing</searchLink><br /><searchLink fieldCode="DE" term="%22Freesound%22">Freesound</searchLink><br /><searchLink fieldCode="DE" term="%22content+based+classification%22">content based classification</searchLink> – Name: Abstract Label: Description Group: Ab Data: Automatic classification of musical instrument is an old research topic in music information retrieval. In this work we address the problem of the classification using musical instrument single note samples from Freesound and we put the accent on the content analysis of the sound and how those content information are connected to the physical characteristics of each instrument. We build a taxonomy based on instrument families and mode of excitation. The musical instruments play a central role in this work and the studies on timbre are used as a methodological base to apply feature selection to our complete set of descriptors, the aim of this is to find which descriptors are relevant to describe a specific instrument. The machine learning then is used as an instrument to evaluate our choices, to identify weakness and problem in the current implementation of audio descriptors. – Name: TypeDocument Label: Document Type Group: TypDoc Data: text – Name: Language Label: Language Group: Lang Data: English – Name: NoteTitleSource Label: Relation Group: SrcInfo Data: https://zenodo.org/communities/smc-master/; https://zenodo.org/records/3786212; oai:zenodo.org:3786212; https://doi.org/10.5281/zenodo.3786212 – Name: DOI Label: DOI Group: ID Data: 10.5281/zenodo.3786212 – Name: URL Label: Availability Group: URL Data: https://doi.org/10.5281/zenodo.3786212<br />https://zenodo.org/records/3786212 – Name: Copyright Label: Rights Group: Cpyrght Data: Creative Commons Attribution 4.0 International ; cc-by-4.0 ; https://creativecommons.org/licenses/by/4.0/legalcode – Name: AN Label: Accession Number Group: ID Data: edsbas.BDD3BA2E |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.5281/zenodo.3786212 Languages: – Text: English Subjects: – SubjectFull: machine learning Type: general – SubjectFull: signal processing Type: general – SubjectFull: Freesound Type: general – SubjectFull: content based classification Type: general Titles: – TitleFull: Automatic Classification of Musical Instrument Samples Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Daniele Scarano IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Type: published Y: 2016 Identifiers: – Type: issn-locals Value: edsbas – Type: issn-locals Value: edsbas.oa |
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