Environmental sound classification (ESC) is one of the fundamental study areas for digital forensics and machine learning. A novel textural feature extractor which is ternary and signum kernelled linear hexadecimal pattern (TSK-LHP) is presented as feature extractor. Multileveled feature extraction method is used and levels are created by discrete wavelet transform (DWT). TSK-LHP generates features from each level. The most distinctive ones are selected by using hybrid feature selector. This hybrid feature selector uses neighborhood component analysis (NCA) and principle component analysis (PCA) together. Therefore, it is called as NPCA. A novel ESC dataset was collected for testing and there are 1211 sounds with 25 classes in this dataset. The proposed method is tested by using four shallow classifiers which are decision tree (DT), linear discriminant (LD), support vector machine (SVM), k nearest neighbor (kNN) and bagged tree (BT). Our proposed method achieved 99.83%, 100.0%, 99.17%, 93.64% and 98.35% classification accuracies by using these classifiers respectively.

环境声音分类（ESC）是数字取证和机器学习的基础研究领域之一。提出了一种新颖的三元和正负整数线性十六进制模式（TSK-LHP）的纹理特征提取器作为特征提取器。使用多级特征提取方法，并通过离散小波变换（DWT）创建级别。TSK-LHP从每个级别生成功能。通过使用混合功能选择器选择最有特色的选项。此混合特征选择器一起使用邻域分量分析（NCA）和主分量分析（PCA）。因此，它被称为NPCA。收集了一个新的ESC数据集进行测试，该数据集中有1211种声音和25个类别。通过使用四个浅分类器（即决策树（DT））对提出的方法进行了测试，线性判别（LD），支持向量机（SVM），k最近邻（kNN）和袋装树（BT）。通过使用这些分类器，我们的方法分别实现了99.83％，100.0％，99.17％，93.64％和98.35％的分类精度。