Abstract Nowadays, many people have suffered from sleep disorders. These diseases affect daily life and can disrupt sanity. Sleep disorders/diseases can be diagnosed by using nocturnal sleep sounds. This work presents an automated nocturnal sound classification method. The proposed nocturnal sound classification method can be used in the automated sleep disease diagnosis process. To propose a highly accurate and cognitive method, a fused feature generation network is proposed. The proposed fused feature generation network extracts both textural features and statistical features together. Therefore, this method is called as fused textural and statistical feature generation network (FusedTSNet). One-dimensional discrete wavelet transform (DWT) is employed to create levels and 7-leveled DWT is applied to nocturnal sounds. Here, DWT is utilized as a pooling/decomposition method to create a multileveled feature generation network. By using the ReliefF iterative neighborhood component analysis (RFINCA), the most valuable features are selected. To demonstrate the success of the FusedTSNet and RFINCA based nocturnal sound classification method, conventional classifiers are used. The proposed FusedTSNet and RFINCA based nocturnal sound classification method were tested on a collected nocturnal sound dataset. This dataset has 700 sounds in 7 classes. Our method achieved a 98.0% classification rate on this dataset. This work clearly indicates that the automated sleep behavior detection can be developed and the success of the proposed FusedTSNet and RFINCA based sound classification method is obviously shown.

中文翻译：

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FusedTSNet：一种基于融合纹理和统计特征生成网络的自动夜间睡眠声音分类方法

摘要 如今，许多人都患有睡眠障碍。这些疾病会影响日常生活，并会破坏理智。睡眠障碍/疾病可以通过使用夜间睡眠声音来诊断。这项工作提出了一种自动化的夜间声音分类方法。所提出的夜间声音分类方法可用于自动睡眠疾病诊断过程。为了提出一种高度准确和认知的方法，提出了融合特征生成网络。提出的融合特征生成网络同时提取纹理特征和统计特征。因此，这种方法被称为融合纹理和统计特征生成网络（FusedTSNet）。一维离散小波变换 (DWT) 用于创建级别，7 级 DWT 用于夜间声音。这里，DWT 被用作池化/分解方法来创建多级特征生成网络。通过使用 ReliefF 迭代邻域分量分析 (RFINCA)，选择最有价值的特征。为了证明基于 FusedTSNet 和 RFINCA 的夜间声音分类方法的成功，使用了传统分类器。所提出的基于 FusedTSNet 和 RFINCA 的夜间声音分类方法在收集的夜间声音数据集上进行了测试。该数据集有 7 个类别的 700 个声音。我们的方法在这个数据集上实现了 98.0% 的分类率。这项工作清楚地表明可以开发自动睡眠行为检测，并且明显显示了所提出的基于 FusedTSNet 和 RFINCA 的声音分类方法的成功。