基于时频分帧能量熵的陶瓷制品敲击声波信号特征识别

针对现有陶瓷制品敲击声波信号特征提取方法中提取的特征代表性降低的问题，该文提出结合最大重叠离散小波包变换(MODWPT)和时频分帧能量熵的特征提取方法。首先采用MODWPT将信号分解为4层，再对每个节点的子信号分帧后计算各个节点的时频分帧能量熵，然后根据能量分布特征选择了前6个节点的时频分帧能量熵特征，最后构建随机森林分类器完成识别。将该方法和MODWPT时频分段能量熵、MODWPT归一化能量特征两种方法进行比较。实验结果表明，相比MODWPT时频分段能量熵、MODWPT归一化能量两种特征提取方法，MODWPT时频分帧能量熵能提升特征的代表性，具有更优的陶瓷制品敲击声波信号特征识别性能，其识别的F₁值达到了98.46%，相比上述两种方法分别提升F₁值3.22%、1.86%。

Coin-tap sound signal characteristics recognition of ceramic products based on time-frequency framing energy entropy

In view of the problems of decreasing of extracted feature representativeness in feature extraction method of ceramic products coin-tap sound signal characteristics Recognition, the feature extraction method combining maximum overlap discrete wavelet transformation (MODWPT) with time-frequency framing energy entropy is proposed. Firstly, the MODWPT method is applied to decompose the coin-tap sound signal to level 4. Then the time-frequency framing energy entropy of each node is calculated after sub-signal framing of each node. And then the time-frequency framing energy entropy features of the first 6 nodes are selected according to the energy distribution feature. Finally, the random forest (RF) classifier is constructed to complete the identification. This method is compared with two methods of MODWPT time-frequency segment energy entropy and MODWPT normalized energy features. The experimental results show that compared with MODWPT time-frequency segment energy entropy and MODWPT normalized energy, MODWPT time-frequency framing energy entropy can improve the representativeness of extracted feature, and has better recognition performance of coin-tap acoustic signal feature of ceramic products. The F1 value of recognition reached 98.5%, and compared with the above two methods, 3.1% and 1.9% are increased respectively.