基于自适应多分支卷积的声学场景分类

doi:10.12305/j.issn.1001-506X.2025.10.03

Abstract

Abstract:

Aiming to address the problem of the model’s insufficient feature representation ability in the acoustic scene classification task, a network architecture based on adaptive multi-branch convolutional optimization is proposed. Firstly, multiple branches are used to extract features independently, and dynamic weights are introduced to adaptively adjust the balance among the branches, enhancing feature perception capability. Secondly, to address the issue of ignoring the relationships among classes during classification in existing models, a coarse-grained classifier is introduced to assist in training the original classification model. The classification process is enhanced by fusing the results. The proposed method is trained and tested on the TUT2020 mobile development dataset. Experimental results show that the accuracy of the proposed method is improved by 6.5% compared with the algorithm before optimization, demonstrating that the proposed method effectively enhances the overall classification performance.

Key words: acoustic scene classification, convolutional neural networks, adaptive feature fusion, hierarchical proposed

CLC Number:

Juan WEI, Dehua HE, Fangli NING. Acoustic scene classification based on adaptive multi-branch convolution[J]. Systems Engineering and Electronics, 2025, 47(10): 3148-3154.

Figures/Tables 10

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References 30

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名称	细粒度分类器架构
输入	Input
卷积层	$\begin{array}{l}5 \times 5 @ 128 \\{\mathrm{B N}}, {\mathrm{RELU}}\end{array} $
阶段1	$ \left(\begin{array}{c}A(3\times 3@128),{\mathrm{BN}}\\ A(1\times 1@128),{\mathrm{BN}}\\ \mathrm{max}\;{\mathrm{pool}}\end{array}\right) $
阶段1	$ \left( \begin{gathered} A(3 \times 3@128),{\mathrm{BN}} \\A(3 \times 3@128),{\mathrm{BN}} \\ \end{gathered} \right) \times 3 $
阶段2	$ \left( \begin{gathered} A(3 \times 3@256),{\mathrm{BN}} \\ A(1 \times 1@256),{\mathrm{BN}} \\ \end{gathered} \right) $
阶段2	$ \left( \begin{gathered} A(1 \times 1@256),{\mathrm{BN}} \\A(1 \times 1@256),{\mathrm{BN}} \\ \end{gathered} \right) \times 3 $
阶段3	$ \left( \begin{gathered}A(1 \times 1@512),{\mathrm{BN}} \\ A(1 \times 1@512),{\mathrm{BN}} \\ \end{gathered} \right) \times 4 $
平均池化层	Average pool
全连接层	$ 1 \times 1@10,{\mathrm{BN}},{\mathrm{softmax}} $
输出层	Output

数据增强方法	准确率
未使用数据增强	0.693
Mixup	0.702
Freq-Mixstyle	0.714
Mixup+Freq-Mixstyle	0.719

模型	A	B	C	S1	S2	S3	S4	S5	S6	平均准确率	预测时间/s
RFRNet	0.791	0.690	0.693	0.682	0.654	0.709	0.693	0.691	0.630	0.692	2.1
AMB_Net	0.809	0.748	0.735	0.724	0.679	0.715	0.700	0.694	0.686	0.719	2.5

模型	A	B	C	S1	S2	S3	S4	S5	S6	平均准确率
粗粒度网络	0.912	0.939	0.909	0.912	0.933	0.909	0.948	0.0.906	0.936	0.923
细粒度网络	0.809	0.748	0.735	0.724	0.679	0.715	0.700	0.694	0.686	0.719
二阶段分类系统	0.824	0.781	0.787	0.761	0.727	0.748	0.748	0.733	0.703	0.757

类别	DCASE2020基线^[29]	RFRNet	细粒度网络	二阶段网络
机场	0.450	0.628	0.676	0.780
公交	0.629	0.874	0.909	0.912
地铁	0.535	0.789	0.801	0.838
地铁站	0.530	0.757	0.771	0.832
公园	0.713	0.732	0.764	0.764
广场	0.449	0.488	0.552	0.596
商场	0.483	0.495	0.505	0.562
步行街	0.298	0.587	0.599	0.650
交通街道	0.799	0.868	0.872	0.875
有轨电车	0.522	0.706	0.733	0.760
平均值	0.541	0.692	0.719	0.757

Acoustic scene classification based on adaptive multi-branch convolution

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References 30

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网络类型	准确率
DCASE2020基线	0.541
RFRNet	0.692
AMFM^[10]	0.713
Trident ResNet^[13]	0.742
NRI_Net^[30]	0.754
二阶段网络	0.757

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