Zhang Ke, Ting Hua-Nong, Choo Yao-Mun
Department of Biomedical Engineering, Faculty of Engineering, Universiti Malaya, Jalan Pantai Baharu, 50603 Kuala Lumpur, Malaysia.
Department of Biomedical Engineering, Faculty of Engineering, Universiti Malaya, Jalan Pantai Baharu, 50603 Kuala Lumpur, Malaysia; Faculty of Medical Engineering, Jining Medical University, University Park, National High-tech Zone, 272067 Jining City, Shandong Province, China.
Comput Methods Programs Biomed. 2024 Mar;245:108043. doi: 10.1016/j.cmpb.2024.108043. Epub 2024 Jan 21.
Conflict may happen when more than one classifier is used to perform prediction or classification. The recognition model error leads to conflicting evidence. These conflicts can cause decision errors in a baby cry recognition and further decrease its recognition accuracy. Thus, the objective of this study is to propose a method that can effectively minimize the conflict among deep learning models and improve the accuracy of baby cry recognition.
An improved Dempster-Shafer evidence theory (DST) based on Wasserstein distance and Deng entropy was proposed to reduce the conflicts among the results by combining the credibility degree between evidence and the uncertainty degree of evidence. To validate the effectiveness of the proposed method, examples were analyzed, and applied in a baby cry recognition. The Whale optimization algorithm-Variational mode decomposition (WOA-VMD) was used to optimally decompose the baby cry signals. The deep features of decomposed components were extracted using the VGG16 model. Long Short-Term Memory (LSTM) models were used to classify baby cry signals. An improved DST decision method was used to obtain the decision fusion.
The proposed fusion method achieves an accuracy of 90.15% in classifying three types of baby cry. Improvement between 2.90% and 4.98% was obtained over the existing DST fusion methods. Recognition accuracy was improved by between 5.79% and 11.53% when compared to the latest methods used in baby cry recognition.
The proposed method optimally decomposes baby cry signal, effectively reduces the conflict among the results of deep learning models and improves the accuracy of baby cry recognition.
当使用多个分类器进行预测或分类时，可能会发生冲突。识别模型误差会导致证据冲突。这些冲突会在婴儿哭声识别中导致决策错误，并进一步降低其识别准确率。因此，本研究的目的是提出一种方法，能够有效减少深度学习模型之间的冲突，并提高婴儿哭声识别的准确率。
提出一种基于瓦瑟斯坦距离和邓熵的改进型德普斯特-谢弗证据理论（DST），通过结合证据之间的可信度和证据的不确定性程度来减少结果之间的冲突。为验证所提方法的有效性，进行了实例分析，并将其应用于婴儿哭声识别。采用鲸鱼优化算法-变分模态分解（WOA-VMD）对婴儿哭声信号进行最优分解。使用VGG16模型提取分解分量的深度特征。采用长短期记忆（LSTM）模型对婴儿哭声信号进行分类。使用改进的DST决策方法获得决策融合。
所提融合方法在对三种类型的婴儿哭声进行分类时，准确率达到90.15%。与现有的DST融合方法相比，提高了2.90%至4.98%。与婴儿哭声识别中使用的最新方法相比，识别准确率提高了5.79%至11.53%。
所提方法对婴儿哭声信号进行了最优分解，有效减少了深度学习模型结果之间的冲突，提高了婴儿哭声识别的准确率。
