Journal of Computer Applications ›› 2022, Vol. 42 ›› Issue (7): 2072-2077.DOI: 10.11772/j.issn.1001-9081.2021050740
• Artificial intelligence • Previous Articles
Wanjun LIU, Jiaming WANG(), Haicheng QU, Libing DONG, Xinyu CAO
Received:
2021-05-10
Revised:
2021-11-05
Accepted:
2021-11-24
Online:
2021-12-31
Published:
2022-07-10
Contact:
Jiaming WANG
About author:
LIU Wanjun, born in 1959, M. S., professor. His research interests include digital image processing, moving target detection and tracking.Supported by:
通讯作者:
王佳铭
作者简介:
刘万军(1959—),男,辽宁锦州人,教授,硕士,CCF高级会员,主要研究方向:数字图像处理、运动目标检测与跟踪基金资助:
CLC Number:
Wanjun LIU, Jiaming WANG, Haicheng QU, Libing DONG, Xinyu CAO. Music genre classification algorithm based on attention spectral-spatial feature[J]. Journal of Computer Applications, 2022, 42(7): 2072-2077.
刘万军, 王佳铭, 曲海成, 董利兵, 曹欣宇. 基于频谱空间域特征注意的音乐流派分类算法[J]. 《计算机应用》唯一官方网站, 2022, 42(7): 2072-2077.
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URL: http://www.joca.cn/EN/10.11772/j.issn.1001-9081.2021050740
预处理方式 | 流派分类准确率 |
---|---|
传统傅里叶变换 | 85.35 |
梅尔频谱 | 87.27 |
Tab.1 Genre classification accuracy of ablation experiment of feature preprocessing
预处理方式 | 流派分类准确率 |
---|---|
传统傅里叶变换 | 85.35 |
梅尔频谱 | 87.27 |
实验编号 | 四重卷积 | 空间注意力 | 残差模块 | 准确率/% |
---|---|---|---|---|
a | — | — | — | 87.27 |
b | — | √ | — | 88.38 |
c | √ | √ | — | 89.01 |
d | — | √ | √ | 90.10 |
e | √ | √ | √ | 91.62 |
Tab.2 Genre classification accuracies in ablation experiment for main modules of model
实验编号 | 四重卷积 | 空间注意力 | 残差模块 | 准确率/% |
---|---|---|---|---|
a | — | — | — | 87.27 |
b | — | √ | — | 88.38 |
c | √ | √ | — | 89.01 |
d | — | √ | √ | 90.10 |
e | √ | √ | √ | 91.62 |
网络 | 流派分类准确率 |
---|---|
GoogLeNet | 81.18 |
ResNet-34B | 84.67 |
VGGNet19 | 86.11 |
AlexNet | 86.26 |
DCNN-SSA | 91.62 |
Tab.3 Genre classification accuracy comparison of different networks on verification set
网络 | 流派分类准确率 |
---|---|
GoogLeNet | 81.18 |
ResNet-34B | 84.67 |
VGGNet19 | 86.11 |
AlexNet | 86.26 |
DCNN-SSA | 91.62 |
网络 | 流派分类准确率 |
---|---|
GoogLeNet | 70.00 |
ResNet-34B | 72.00 |
VGGNet19 | 76.00 |
AlexNet | 76.00 |
DCNN-SSA | 82.00 |
Tab.4 Genre classification accuracy comparison of different networks on test set
网络 | 流派分类准确率 |
---|---|
GoogLeNet | 70.00 |
ResNet-34B | 72.00 |
VGGNet19 | 76.00 |
AlexNet | 76.00 |
DCNN-SSA | 82.00 |
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