Application of Residual Network to Infant Crying Recognition
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摘要:
该文使用语谱图结合残差网络的深度学习模型进行婴幼儿哭声的识别，使用婴幼儿哭声与非哭声样本比例均衡的语料库，经过五折交叉验证，与支持向量机(SVM)，卷积神经网络(CNN)，基于Gammatone滤波器的听觉谱残差网络(GT-Resnet)3种模型相比，基于语谱图的残差网络取得了最优结果，F1-score达到0.9965，满足实时性要求，证明了语谱图在婴幼儿哭声识别任务中能直观地反映声学特征，基于语谱图的残差网络是解决婴幼儿哭声识别任务的优秀方法。
Abstract:The deep learning model based on the residual network and the spectrogram is used to recognize infant crying. The corpus has balanced proportion of infant crying and non-crying samples. Finally, through the 5-fold cross validation, compared with three models of Support Vector Machine (SVM), Convolutional Neural Network (CNN) and the cochleagram residual network based on Gammatone filters (GT-Resnet), the spectrogram based residual network gets the best F1-score of 0.9965 and satisfies requirements of real time. It is proved that the spectrogram can react acoustics features intuitively and comprehensively in the recognition of infant crying. The residual network based on spectrogram is a good solution to infant crying recognition problem.
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Key words:
- Infant crying recognition /
- Deep learning /
- Residual network /
- Spectrogram
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表 3 SVM不同核函数性能比较
核函数类型 F1-score 参数 线性核函数 0.8717 c=0.68 多项式核函数 0.9316 c=0.30, g=0.35, r=–0.20, d=3.00 高斯核函数 0.9458 c=0.98, g=1.71 Sigmod核函数 0.8874 c=5.00, g=0.04, r=1.80 
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表 4 不同层数CNN性能对比
CNN模型 输入特征 F1-score CNN-4-MEL 40×128Mel语谱图 0.9184 CNN-4-227 227×227语谱图 0.9233 CNN-4 128×128语谱图 0.9229 CNN-5-227 227×227语谱图 0.9482 CNN-5 128×128语谱图 0.9489 CNN-6 128×128语谱图 0.9365 CNN-7 128×128语谱图 0.9398
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表 5 模型性能对比
模型 网络结构 输入特征 生成模型大小(MB) 平均测试时间(s) F1-score SVM 单层网络 统计特征 0.7 0.0910+0.0001 0.9458 CNN-5 4conv+1fc 语谱图 10 0.1251+0.0093 0.9489 Resnet15 3resblock+1fc 语谱图 48 0.1251+0.0281 0.9836 Resnet19 4resblock+1fc 语谱图 87 0.1251+0.0315 0.9965 Resnet27 6resblock+1fc 语谱图 171 0.1251+0.0355 0.9965 GT-Resnet15 3resblock+1fc 听觉谱 48 0.1933+0.0218 0.9803 GT-Resnet19 4resblock+1fc 听觉谱 87 0.1933+0.0237 0.9782 GT-Resnet27 6resblock+1fc 听觉谱 171 0.1933+0.0285 0.9719 注：平均测试时间=特征提取时间+模型预测时间
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