Browse > Article
http://dx.doi.org/10.13067/JKIECS.2020.15.6.987

Optimization of the Kernel Size in CNN Noise Attenuator  

Lee, Haeng-Woo (Dept. of Information Communication Engineering, Namseoul University)
Publication Information
The Journal of the Korea institute of electronic communication sciences / v.15, no.6, 2020 , pp. 987-994 More about this Journal
Abstract
In this paper, we studied the effect of kernel size of CNN layer on performance in acoustic noise attenuators. This system uses a deep learning algorithm using a neural network adaptive prediction filter instead of using the existing adaptive filter. Speech is estimated from a single input speech signal containing noise using a 100-neuron, 16-filter CNN filter and an error back propagation algorithm. This is to use the quasi-periodic property in the voiced sound section of the voice signal. In this study, a simulation program using Tensorflow and Keras libraries was written and a simulation was performed to verify the performance of the noise attenuator for the kernel size. As a result of the simulation, when the kernel size is about 16, the MSE and MAE values are the smallest, and when the size is smaller or larger than 16, the MSE and MAE values increase. It can be seen that in the case of an speech signal, the features can be best captured when the kernel size is about 16.
Keywords
Noise Reduction; Deep Learning; Convolutional Neural Network(CNN); Kernel Size;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
연도 인용수 순위
1 S. F. Boll, "Suppression of acoustic noise in speech using spectral subtraction," IEEE Trans. Acoust., Speech, Signal Processing, vol. ASSP-29, Apr. 1979, pp. 113-120.   DOI
2 A. Schaub and P. Schaub, "Spectral sharpening for speech enhancement/noise reduction," Proc. of Int. Conf. on Acoust., Speech, Signal Processing, vol. 2, May 1991, pp. 993-996.
3 J. S. Lim and A. V. Oppenheim, "All-pole modeling of degraded speech," IEEE Trans. Acoust., Speech, Signal Processing, vol. ASSP-26, Jun. 1978, pp. 197-210.
4 J. Hansen and M. Clements, "Constrained iterative speech enhancement with to speech recognition," IEEE Trans. Acoust., Speech, Signal Processing, vol. ASSP-39, no. 4, Apr. 1989, pp. 21-27.
5 J. Choi, "Noise Reduction Algorithm in Speech by Wiener Filter," J. of the Korea Institute of Electronic Communication Sciences, vol. 8, Sep. 2013, pp. 1293-1298.   DOI
6 J. S. Lim, A. V. Oppenheim and L. D. Braida, "Evaluation of an adaptive comb filtering method for enhancing speech degraded by white noise addition," IEEE Trans. Acoust., Speech, Signal Processing, vol. ASSP-26, no. 4, Apr. 1991, pp. 354-358.
7 S. F. Boll and D. C. Pulsipher, "Suppression of acoustic noise in speech using two microphone adaptive noise cancellation," IEEE Trans. Acoust., Speech, Signal Processing, vol. ASSP-28, no. 6, Dec. 1989, pp. 752-753.
8 J. Schmidhuber, "Deep learning in neural networks: An overview," Neural Networks, vol. 61, 2015, pp. 85-117.   DOI
9 O. S. Kwon, "Study on Efficient Adaptive Controller for Attenuation of Engine Noises in a Car," J. of the Korea Institute of Electronic Communication Sciences, vol. 9, Sep. 2014, pp. 983-989.   DOI
10 M. R. Sambur, "Adaptive noise canceling for speech signals," IEEE Trans. Acoust., Speech, Signal Processing, vol. ASSP-26, Oct. 1978, pp. 419-423.   DOI
11 J. Choi, "Speech and Noise Recognition System by Neural Network," J. of the Korea Institute of Electronic Communication Sciences, vol. 5, Aug. 2010, pp. 357-362.
12 W. A. Harrison, J. S. Lim, and E. Singer, "A new application of adaptive noise cancellation," IEEE Trans. Acoust., Speech, Signal Processing, vol. ASSP-34, Feb. 1986, pp. 21-27.
13 Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner "Gradient-Based Learning Applied to Document Recognition," Proceedings of the IEEE, vol. 86, no. 11, Nov. 1998, pp. 2278-2324.   DOI
14 D. Rumelhart, G. Hinton, and R. Williams, "Learning representations by back-propagating errors," Cognitive modeling, vol. 5, 1988, pp. 3.