Journal of Shandong University(Engineering Science) ›› 2020, Vol. 50 ›› Issue (2): 108-117.doi: 10.6040/j.issn.1672-3961.0.2019.419

• Machine Learning & Data Mining • Previous Articles     Next Articles

Abnormal sound detection of washing machines based on deep learning

Chunyang LI(),Nan LI*(),Tao FENG,Zhuhe WANG,Jingkai MA   

  1. School of Material and Mechanical Engineering, Beijing Technology and Business University, Beijing 100048, China
  • Received:2019-07-22 Online:2020-04-20 Published:2020-04-16
  • Contact: Nan LI E-mail:chuny6896@163.com;linan@th.btbu.edu.cn
  • Supported by:
    国家自然科学基金资助项目(61877002);国家自然科学基金资助项目(51405005)

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Abstract:

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.

Key words: audio classification, convolution neural network, generative adversarial networks, deep learning

CLC Number: 

  • TP181

Fig.1

Sound collectionof washing machine on the production line"

Fig.2

Production of original sound data set"

Fig.3

Training of convolutional neural network by enhanced dataset"

Fig.4

Network diagram of ADCGAN"

Fig.5

Structure diagram of abnormal sound detection model of washing machine based on CNN"

Fig.6

The variation of Vloss with training times"

Fig.7

The variation of Dloss and Glosswith training times"

Table 1

Networktraining parameters of washing machine abnormalsound recognition"

学习速率η Dropout概率系数p 平滑参数 批量大小 迭代次数
0.000 1 0.6 0.25 200 20 000

Fig.8

Classification accuracy of abnormal sound recognition network of washing machine trained with original data set"

Fig.9

Classification accuracy of abnormal sound recognition network of washing machine trained with augmented data set"

Fig.10

Test accuracy of CNN model trained by datasets of different sampling frequencies"

Table 2

Cross-validation Accuracy of Each Models"

模型 测试精度
CNN 0.999
RNN 0.687
DNN 0.992
SVM 0.681
LSTM 0.995
MLP 0.994

Fig.11

Comparison of waveforms of normal sound data with and without noise"

Fig.12

Comparison of waveforms with and without noise in abnormal sound data"

Table 3

Cross-validationaccuracy for each dataset of CNN"

数据集 测试精度
原始数据集 0.923
加噪数据集(SNR=10 dB) 0.902
增强训练集 0.999
1 MADAIN M, AL-MOSAIDEN A, AL-KHASSAWENEH M. Fault diagnosis in vehicle engines using sound recognition techniques[C]// Proceedings of the 2010 IEEE Conference on Electro/Information Technology. New York, USA: IEEE, 2010: 1-4.
2 WANG Y, NEVES L, METZE F. Audio-based multi-media event detection using deep recurrent neural networks[C]//the 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). New York, USA: IEEE, 2016: 2742-2746.
3 PARASCANDOLO G, HUTTUNEN H, VIRTANEN T. Recurrent neural networks for polyphonic sound event detection in real life recordings[C]//the 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). NewYork, USA: IEEE, 2016: 6440-6444.
4 ZHANG H M, MCLOUGHLIN I, SONG Y. Robust sound event recognition using convolutional neural networks[C]//the 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). New York, USA: IEEE, 2016: 559-563.
5 PHAN H, HERTEL L, MAASS M, et al. Robust audio event recognition with 1-max pooling convolutional neural networks[C]//Proceedings of the 17th Annual Conference of the International Speech Communication Association (INTERSPEECH).San Francisco, USA: Interspeech, 2016: 3653-3657.
6 PICZAK K J. Environmental sound classification with convolutional neural networks[C]//the 2015 IEEE 25th International Workshop on Machine Learning for Signal Processing (MLSP). Boston, USA: IEEE, 2015: 1-6.
7 JEON S, SHIN JW, LEE YJ, et al. Empirical study of drone sound detection in real-life environment with deep neural networks[J]. arXiv: Sound, 2017.https://arxiv.org/abs/1701.05779.
8 SALAMON J, BELLO J. P. Feature learning with deep scattering for urban sound analysis[C]// 25th 2015 23rd European Signal Processing Conference(EUSIP-CO).Nice, France: IEEE, 2015: 724-728.
9 JIA F , LEI Y G , GUO L , et al. A neural network constructed by deep learning technique and its application to intelligent fault diagnosis of machines[J]. Neurocom-puting, 2017, 272, 619- 628.
10 STOWELL D, CLAYTON D. Acoustic event detection for multiple overlapping similar sources[C]//2015 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA).New York, USA: IEEE, 2015: 1-5.
11 SADLIER D A , O'CONNOR N E . Event detection in field sports video using audio-visual features and a support vector machine[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2005, 15 (10): 1225- 1233.
doi: 10.1109/TCSVT.2005.854237
12 LI B, HE M Y, CHENG X L, et al. Skeleton based action recognition using translation-scale invariant image mapping and multi-scale deep CNN[C]//Proceedings of 2017 IEEE International Conference on Multimedia & Expo Workshops (ICMEW). New York, USA: IEEE, 2017: 601-604.
13 XU Y F , ZHANG Y , WANG H G , et al. Deep convolutional neural networks and data augmentation for environmental sound classification[J]. IEEE Signal Processing Letters, 2017, 24 (3): 279- 283.
14 NARASIMHAN R, FERN X L, RAICH R. Simultaneous segmentation and classification of bird song using CNN[C]//2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).New Orleans, USA: IEEE, 2017: 146-150.
15 ANTONIOU A, STORKEY A, EDWARDS H. Data augmentation generative adversarial networks[J]. arXiv: Computer Vision and Pattern Recognition, 2018. https://arxiv.org/abs/1711.04340v3.
16 GOODFELLOW I, POUGET-ABADIE J, MIRZA M. Generative Adversarial Networks[J]. arXiv: Machine Learning, 2014.https://arxiv.org/abs/1406.2661.
17 DENTON E, CHINTALA S, FERGUS R. Deep generative image models using a laplacian pyramid of adversarial networks[C]// 28th International Conference on Neural Information Processing. California, USA: NIPS, 2015: 1486-1494.
18 POUYANFAR S, TAO Y, MOHAN A. Dynamic sampling in convolutional neural networks for imbalanced data classification[C]// 2018 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR). New York, USA: IEEE, 2018: 112-117.
19 RADFORD A, METZ L, CHINTALA S. Unsupervised representation learning with deep convolutional generative adversarial networks[J]. arXiv: Computer Vision and Pattern Recognition, 2015. https://arxiv.org/abs/1511.06434.
20 STOWELL D, WOOD M, STYLIANOU Y, et al. Bird detection in audio: A survey and a challenge[C]// 2016 IEEE 26th International Workshop on Machine Learning for Signal Processing (MLSP). New York, USA: IEEE, 2016: 1-6.
21 ABDEL-HAMID O , MOHAMED A , JIANG H , et al. Convolutional neural networks for speech recognition[J]. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 2014, 22 (10): 1533- 1545.
doi: 10.1109/TASLP.2014.2339736
22 GLOROT X, BORDES A, BENGIO Y. Deep sparse rectifier neural networks[C]// Proceedings of the 14th International Conference on Artificial Intelligence and Statistics (AISTATS).Ft.Lauderdale, USA: Neural Networks, 2011: 315-323.
23 IOFFE S, SZEGEDY C. Batch normalization: Accelerating deep network training by reducing internal covariate shift[C]// Proceedings of 32nd International Conference on International Conference on Machine Learning. New York, USA: ACM, 2015: 448-456.
24 SALIMANS T, GOODFELLOW I, ZAREMBA W, et al. Improved techniques for training GANs[J]. arXiv: Neural and Evolutionary Computing, 2016. https://arxiv.org/abs/1606.03498v1.
25 SALAMON J , BELLO J . Deep convolutional neural net-works and data augmentation for environmental sound classification[J]. IEEE Signal Processing Letters, 2017, 24 (3): 279- 283.
26 CUI X D, GOEL V, KINGSBURY B. Data augmentation for deep neural network acoustic modeling[C]// 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). New York, USA: IEEE, 2014: 1469-1477.
27 KONG Q Q, XU Y, MARK D. Joint detection and classification convolutional neural network on weakly labelled bird audio detection[C]// Proceedings of 2017 25th European Signal Processing Conference (EUSIPCO). Kos: Greece, IEEE, 2017: 1749-1753.
28 TANG B , TU Y , ZHANG S Y . Digital signal modulation classification with data augmentation using generative adversarial nets in cognitive radio networks[J]. IEEE Access, 2018, (6): 15713- 15722.
29 MESAROS A, HEITTOLA T, VIRTANEN T. TUT database for acoustic scene classification and sound event detection[C]//Proceedings of 2016 24th European Signal Processing Conference (EUSIPCO).Hungary, Budapest: IEEE, 2016: 1128-1132.
30 ZHENG Q H , YANG M Q , YANG J J . Improvement of generalization ability of deep CNN via implicit regularization in two-stage training process[J]. IEEE Access, 2018, (6): 15844- 15869.
31 MCLOUGHLIN I , ZHANG H M , XIE Z P . Robust sound event classification using deep neural networks[J]. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 2015, 23 (3): 540- 552.
doi: 10.1109/TASLP.2015.2389618
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