This paper proposes a robust deep learning framework used for classifying anomaly of respiratory cycles. Initially, our framework starts with front-end feature extraction step. This step aims to transform the respiratory input sound into a two-dimensional spectrogram where both spectral and temporal features are well presented. Next, an ensemble of C- DNN and Autoencoder networks is then applied to classify into four categories of respiratory anomaly cycles. In this work, we conducted experiments over 2017 Internal Conference on Biomedical Health Informatics (ICBHI) benchmark dataset. As a result, we achieve competitive performances with ICBHI average score of 0.49, ICBHI harmonic score of 0.42.

中文翻译：

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深度学习框架可用于预测呼吸音异常

本文提出了一个强大的深度学习框架，用于对呼吸周期异常进行分类。最初，我们的框架从前端特征提取步骤开始。此步骤旨在将呼吸输入声音转换为二维频谱图，其中频谱和时间特征都很好地呈现出来。接下来，然后将C-DNN和自动编码器网络集成在一起，以将其分为呼吸异常周期的四类。在这项工作中，我们在2017年生物医学健康信息学内部会议（ICBHI）基准数据集上进行了实验。结果，我们获得了具有竞争力的性能，ICBHI平均得分为0.49，ICBHI谐波得分为0.42。