Baby Cry Recognition by BCRNet Using Transfer Learning and Deep Feature Fusion

被引:3
作者
Zhang, Ke [1 ]
Ting, Hua-Nong [1 ,2 ]
Choo, Yao-Mun [3 ]
机构
[1] Univ Malaya, Fac Engn, Dept Biomed Engn, Kuala Lumpur 50603, Malaysia
[2] Jining Med Univ, Fac Med Engn, Jining 272067, Shandong, Peoples R China
[3] Univ Malaya, Fac Med, Dept Paediat, Kuala Lumpur 50603, Malaysia
关键词
Baby cry; recognition; transfer learning; autoencoder; feature fusion; deep neural network; CLASSIFICATION;
D O I
10.1109/ACCESS.2023.3330789
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Deep learning theory has made remarkable advancements in the field of baby cry recognition, significantly enhancing its accuracy. Nonetheless, existing research faces two challenges. Firstly, the limited size of the database increases the risk of overfitting for a deep learning model. Secondly, the integration of multi-domain features has been neglected. To address these issues, a novel approach called BCRNet is proposed, which combines transfer learning and feature fusion. The BCRNet model takes multi-domain features as input and extracts deep features using a transfer learning model. Subsequently, a multilayer autoencoder is utilized for feature reduction, and a Support Vector Machine (SVM) is employed to select the transfer learning model with the highest classification accuracy. Then two features are concatenated to form fused features. Finally, the fused features are fed into a deep neural network for classification. Experimental results show that the proposed model is effective in mitigating the model overfitting problem due to small datasets. The fused features of the proposed method are better than the existing methods using single domain features.
引用
收藏
页码:126251 / 126262
页数:12
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