Application of Multilevel Local Feature Coding in Music Genre Recognition

When the current method is used to recognize music genre style, the extracted features are not fused, which leads to poor recognition effectiveness. Therefore, the application research based on multilevel local feature coding in music genre recognition is proposed. Features of music are extracted fr...

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Bibliographic Details
Main Authors: Kailing, D. (Author), Wu, M. (Author), Xiao, Y. (Author), Zhang, Q. (Author)
Format: Article
Language:English
Published: Hindawi Limited 2022
Subjects:
Online Access:View Fulltext in Publisher
LEADER 02096nam a2200313Ia 4500
001 10.1155-2022-3627831
008 220425s2022 CNT 000 0 und d
020 |a 1024123X (ISSN) 
245 1 0 |a Application of Multilevel Local Feature Coding in Music Genre Recognition 
260 0 |b Hindawi Limited  |c 2022 
856 |z View Fulltext in Publisher  |u https://doi.org/10.1155/2022/3627831 
520 3 |a When the current method is used to recognize music genre style, the extracted features are not fused, which leads to poor recognition effectiveness. Therefore, the application research based on multilevel local feature coding in music genre recognition is proposed. Features of music are extracted from timbre, rhythm, and pitch, and the extracted features are fused based on D-S evidence theory. The fused music features are input into the improved deep learning network, and the storage system structure is determined from the advantages of cloud storage availability, manageability, and expansibility. It is divided into four modules: storage layer, management layer, structure layer, and access layer. The model of music genre style recognition is constructed to realize the application research based on multilevel local feature coding in music genre recognition. The experimental results show that the recognition accuracy of the proposed method is always at a high level, and the mean square error positively correlated with the number of beats. After segmentation, the waveform is denser, which has a good application effect. © 2022 Yangxin Xiao et al. 
650 0 4 |a Application research 
650 0 4 |a 'current 
650 0 4 |a D S evidence theory 
650 0 4 |a Deep learning 
650 0 4 |a Feature coding 
650 0 4 |a Learning network 
650 0 4 |a Local feature 
650 0 4 |a Mean square error 
650 0 4 |a Multilevels 
650 0 4 |a Music genre 
650 0 4 |a Storage systems 
650 0 4 |a Systems Structure 
700 1 |a Kailing, D.  |e author 
700 1 |a Wu, M.  |e author 
700 1 |a Xiao, Y.  |e author 
700 1 |a Zhang, Q.  |e author 
773 |t Mathematical Problems in Engineering