Detecting Mechanical Vibrations in Televisions via Audio Spectrogram Classification
Resumo
This paper presents a method for contactless detec tion of mechanical vibrations in televisions through audio spec trogram classification, utilizing Convolutional Neural Networks. The model was trained on a dataset containing simulated samples and demonstrated high accuracy, with excellent learning curves observed during training. In further evaluation with real samples the model performed well, achieving F1-Score rate of 99,02% in the test partition, confirming its potential for use in preventive maintenance processes and in addressing issues in televisions and other audio-dependent equipment, thereby enhancing the efficiency and quality of service.
Palavras-chave:
audio classification, anomaly detection, mechanical vibration, deep learning, convolutional neural network
Referências
L. Hughes, Y. K. Dwivedi, N. P. Rana, M. D. Williams et al., “Perspectives on the future of manufacturing within the industry 4.0 era,” Production Planning & Control, vol. 33, pp. 138–158, 2022.
S.-H. Huang and Y.-C. Pan, “Automated visual inspection in the semiconductor industry: A survey,” Computers in industry, vol. 66, 2015.
R. L. Silva, M. Rudek, A. L. Szejka, and O. C. Junior, “Machine vision systems for industrial quality control inspections,” in Product Lifecycle Management to Support Industry 4.0, 2018.
J. Villalba-Diez, D. Schmidt, R. Gevers, J. Ordieres-Meré, M. Buchwitz, and W. Wellbrock, “Deep learning for industrial computer vision quality control in the printing industry 4.0,” Sensors, vol. 19, 2019.
I. Kastelan and M. Katona, “Automated optical inspection system for digital tv sets,” EURASIP Journal on Advances in Signal Processing, 2011.
P. Pham, J. Li, J. Szurley, and S. Das, “Eventness: Object detection on spectrograms for temporal localization of audio events,” in 2018 IEEE ICASSP, 2018.
G. Z. Felipe, Y. Maldonado, G. d. Costa, and L. G. Helal, “Acoustic scene classification using spectrograms,” in 2017 36th International Conference of the Chilean Computer Science Society (SCCC), 2017, pp. 1–7.
W. Stefan, G. André, and L. Alexander, “Generalized multiple sweep measurement,” Journal of the Audio Engineering Society, no. 7767, 2009.
L. Wyse, “Audio spectrogram representations for processing with convolutional neural networks,” arXiv preprint arXiv:1706.09559, 2017.
M. Tan and Q. V. Le, “Efficientnetv2: Smaller models and faster training,” CoRR, vol. abs/2104.00298, 2021.
I. Goodfellow, Y. Bengio, and A. Courville, Deep learning. MIT press, 2016.
M. Tan and Q. Le, “Efficientnetv2: Smaller models and faster training,” in International Conference on Machine Learning. PMLR, 2021, pp. 10 096–10 106.
R. R. Selvaraju, M. Cogswell, R. Das, Abhishek Vedantam et al., “Grad-cam: Visual explanations from deep networks via gradient-based localization,” in IEEE international conference on computer vision, 2017, pp. 618–626.
M. Sandler, A. G. Howard, M. Zhu, A. Zhmoginov, and L. Chen, “Inverted residuals and linear bottlenecks: Mobile networks for classification, detection and segmentation,” CoRR, 2018.
J. Hu, L. Shen, S. Albanie, G. Sun, and E. Wu, “Squeeze-and-excitation networks,” 2019.
A. F. Agarap, “Deep learning using rectified linear units (relu),” arXiv preprint arXiv:1803.08375, 2018.
“tf.keras.layers.globalaveragepooling2d: Large-scale machine learning on heterogeneous systems,” 2015.
G. K. Pandey and S. Srivastava, “Resnet-18 comparative analysis of various activation functions for image classification,” in 2023 ICICT, 2023.
L. Gao, X. Zhang, T. Yang, B. Wang, and J. Li, “The application of resnet-34 model integrating transfer learning in the recognition and classification of overseas chinese frescoes,” Electronics, 2023.
C. Yu, G. Ding, and C. Yan, “Study on mini-xception-based improved lightweight expression detection model,” in International Conference on Control and Intelligent Robotics, 2022.
F. Chollet, “Xception: Deep learning with depthwise separable convolutions,” in IEEE CVPR, 2017.
S.-H. Huang and Y.-C. Pan, “Automated visual inspection in the semiconductor industry: A survey,” Computers in industry, vol. 66, 2015.
R. L. Silva, M. Rudek, A. L. Szejka, and O. C. Junior, “Machine vision systems for industrial quality control inspections,” in Product Lifecycle Management to Support Industry 4.0, 2018.
J. Villalba-Diez, D. Schmidt, R. Gevers, J. Ordieres-Meré, M. Buchwitz, and W. Wellbrock, “Deep learning for industrial computer vision quality control in the printing industry 4.0,” Sensors, vol. 19, 2019.
I. Kastelan and M. Katona, “Automated optical inspection system for digital tv sets,” EURASIP Journal on Advances in Signal Processing, 2011.
P. Pham, J. Li, J. Szurley, and S. Das, “Eventness: Object detection on spectrograms for temporal localization of audio events,” in 2018 IEEE ICASSP, 2018.
G. Z. Felipe, Y. Maldonado, G. d. Costa, and L. G. Helal, “Acoustic scene classification using spectrograms,” in 2017 36th International Conference of the Chilean Computer Science Society (SCCC), 2017, pp. 1–7.
W. Stefan, G. André, and L. Alexander, “Generalized multiple sweep measurement,” Journal of the Audio Engineering Society, no. 7767, 2009.
L. Wyse, “Audio spectrogram representations for processing with convolutional neural networks,” arXiv preprint arXiv:1706.09559, 2017.
M. Tan and Q. V. Le, “Efficientnetv2: Smaller models and faster training,” CoRR, vol. abs/2104.00298, 2021.
I. Goodfellow, Y. Bengio, and A. Courville, Deep learning. MIT press, 2016.
M. Tan and Q. Le, “Efficientnetv2: Smaller models and faster training,” in International Conference on Machine Learning. PMLR, 2021, pp. 10 096–10 106.
R. R. Selvaraju, M. Cogswell, R. Das, Abhishek Vedantam et al., “Grad-cam: Visual explanations from deep networks via gradient-based localization,” in IEEE international conference on computer vision, 2017, pp. 618–626.
M. Sandler, A. G. Howard, M. Zhu, A. Zhmoginov, and L. Chen, “Inverted residuals and linear bottlenecks: Mobile networks for classification, detection and segmentation,” CoRR, 2018.
J. Hu, L. Shen, S. Albanie, G. Sun, and E. Wu, “Squeeze-and-excitation networks,” 2019.
A. F. Agarap, “Deep learning using rectified linear units (relu),” arXiv preprint arXiv:1803.08375, 2018.
“tf.keras.layers.globalaveragepooling2d: Large-scale machine learning on heterogeneous systems,” 2015.
G. K. Pandey and S. Srivastava, “Resnet-18 comparative analysis of various activation functions for image classification,” in 2023 ICICT, 2023.
L. Gao, X. Zhang, T. Yang, B. Wang, and J. Li, “The application of resnet-34 model integrating transfer learning in the recognition and classification of overseas chinese frescoes,” Electronics, 2023.
C. Yu, G. Ding, and C. Yan, “Study on mini-xception-based improved lightweight expression detection model,” in International Conference on Control and Intelligent Robotics, 2022.
F. Chollet, “Xception: Deep learning with depthwise separable convolutions,” in IEEE CVPR, 2017.
Publicado
06/11/2024
Como Citar
FABRICIO, Romulo; PIMENTEL, Agemilson; BELEM, Ruan; SOUSA, Anderson; MARTINHO, Laura; ARAÚJO, Leo; SILVA, Luan; SOUSA, Osmar.
Detecting Mechanical Vibrations in Televisions via Audio Spectrogram Classification. In: WORKSHOP DE VISÃO COMPUTACIONAL (WVC), 19. , 2024, Rio Paranaíba/MG.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2024
.
p. 7-12.
DOI: https://doi.org/10.5753/wvc.2024.34005.
