| 武雅琴,张佳庆,张涛.数据增强和复杂特征优化的类不平衡病理嗓音检测[J].,2025,44(1):234-244 |
| 数据增强和复杂特征优化的类不平衡病理嗓音检测 |
| Class-Imbalanced Pathological Voice Detection with Data Augmentation and Complex Feature Optimization |
| 投稿时间：2024-08-14 修订日期：2024-12-25 |
| 中文摘要: |
| 本文以提高病理嗓音多分类准确性为目标，构建了一种基于数据增强和复杂特征优化的类不平衡病理嗓音检测系统。首先，本文对32种声学特征进行分析并将其归类为时域类特征和频域类特征；然后，采用改进的合成少数类过采样技术对数据集进行增广与均衡处理；其次，结合高效相关性特征选择算法和盒图对多维声学特征进行融合优化，综合评估各特征的判别能力；最后基于随机森林分类器，详细分析和验证不同特征组合的分类性能。结果表明，本文提出的融合优化特征集（To、Fatr、Jita、sAPQ、vAm、NHR）在随机森林分类器下，对声带小结、息肉、水肿及麻痹四种病理嗓音的分类性能表现优异，取得了88.6%的分类准确率、88.4%的召回率、88.4%的F1分数和99.7%的AUC值。 |
| 英文摘要: |
| This paper aims to enhance the accuracy of pathological voice classification by developing a class-imbalanced pathological voice detection system based on the data augmentation and complex feature optimization. Firstly, thirty-two speech features are analyzed and grouped into two categories: time-domain features and frequency-domain features. Secondly, an improved synthetic minority over-sampling technique is employed to augment and balance the dataset. Next, both the efficient correlation-based feature selection algorithm and the boxplot method are applied to optimize and integrate multidimensional speech features, providing a comprehensive evaluation of the discriminative ability of each feature. Finally, the classification performance of different feature combinations is analyzed and verified in detail using the Random Forest classifier. Experimental results demonstrate that the optimized feature set (To, Fatr, Jita, sAPQ, vAm, NHR) exhibits excellent classification performance for four voice disorders, including vocal nodules, polyps, edema, and paralysis, achieving a classification accuracy of 88.6%, a recall rate of 88.4%, an F1 score of 88.4%, and an AUC of 99.7%. |
| DOI： |
| 中文关键词: 病理嗓音 数据增强 复杂特征 高效相关性特征选择 盒图 |
| 英文关键词: Pathological voice Data augmentation Complex features Efficient Correlation-based Feature Selection Box plot |
| 基金项目:国家自然科学基金项目（面上项目，重点项目，重大项目） |
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