基于深度学习的洗衣机异常音检测

基于卷积神经网络框架，提出一种洗衣机异音识别模型，根据卷积神经网络显著特征提取能力和平移不变性，学习洗衣机的异音特征，实现生产线洗衣机的异音自动智能识别。给出完整的过程解决训练数据集的建立、数据样本不平衡等问题。提出一种用于数据增强的网络模型——音频深度卷积生成对抗网络解决训练样本的稀缺性问题。该模型对传统的深度卷积生成对抗网络进行改进，以更好地适应工业音频的生成。利用该模型能够对原始数据进行扩展，生成洗衣机异音增强数据集，在该数据集的基础上进行卷积神经网络训练，经测试准确率达到0.999。利用添加背景噪声信号的数据集测试洗衣机异音识别模型的泛化能力，正确识别率达到0.902，表明该网络在识别洗衣机异音方面具有良好的鲁棒性。

Abnormal sound detection of washing machines based on deep learning

Based on the convolutional neural network (CNN) framework, a model for abnormal sounds recognition of washing machine was proposed. According to the remarkable feature extraction ability and translation invariance of convolutional neural network, the abnormal sound features of washing machines were learned, so as to achieve the purpose of the automatic intelligent recognition of abnormal sounds for washing machines in production line. This method provided a complete process to solve the problems of training datasets establishment and data imbalance. A network model for data augmentation called advanced deep convolution generated adversarial network (ADCGAN)was proposed to solve the problem of training data scarcity. The traditional deep convolution generated adversarial network (DCGAN) model was improved to better adapt to the generation of industrial sounds. This model could be used to extend the original data and generate the abnormal sound augmented datasets of washing machine. The augmented datasets was used to train the convolutional neural network, and the test accuracy reached 0.999. The generalization ability of abnormal sounds recognition model for washing machine network was tested by using the data set with background noise signal added. The correct recognition rate reached 0.902, which indicated that this network had good robustness in recognizing abnormal noises of washing machines.