Music Classification Method Using Big Data Feature Extraction and Neural Networks.
From the cassette era to the CD era to the digital music era, the quantity of music has grown rapidly. People cannot easily search for the desired music without classifying enormous music resources and developing a successful music retrieval system. By examining users' historical listening patterns...
| Publicado en: | Journal of Environmental & Public Health pp. 1 - 9 |
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| Autores principales: | , |
| Formato: | Journal Article |
| Publicado: |
Wiley-Blackwell
7/30/2022
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=158264676&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 158264676 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 16879805 9034 jtl: Journal of Environmental & Public Health issn: 16879805 maglogo: N pubinfo: dt: 7/30/2022 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 158264676 158264676 NLM35942148 10.1155/2022/5749359 NLM35942148 158264676 ppf: 1 ppct: 8 formats: tig: atl: Music Classification Method Using Big Data Feature Extraction and Neural Networks. aug: au: Li, Xiabin Li, Jin affil: Lingnan Normal University, Zhanjiang 524048, China sug: subj: Music Algorithms ab: From the cassette era to the CD era to the digital music era, the quantity of music has grown rapidly. People cannot easily search for the desired music without classifying enormous music resources and developing a successful music retrieval system. By examining users' historical listening patterns for personalised recommendations, the music recommendation algorithm can lessen message fatigue for users and enhance user experience. Relying on manual labelling is how traditional music is classified. It would be inefficient and unrealistic to attempt to classify music using manual labelling in the age of big data. Feature extraction and neural networks are the tools employed in this paper. The model's parameters can be trained using conventional gradient descent techniques, and the model's trained convolution neural network can learn the image's features and finish the extraction and classification of the features. This algorithm is 12 percent superior to the conventional algorithm, according to the research in this paper. It has strong ability and is appropriate for widespread implementation with the same number of iterations. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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