Noise reduction system by using CNN deep learning model

Haengwoo Lee


In this paper, we propose a new algorithm to reduce the acoustic noise of hearing aids. This algorithm improves the noise reduction performance by the deep learning algorithm using the neural network adaptive prediction filter instead of the existing adaptive filter. The speech is estimated from a single input speech signal containing noise using a 80-neuron, 16-filter convolutional neural network(CNN) filter and an error backpropagation algorithm. This is by using the quasi-periodic property of the voiced section in the speech signal, and it is possible to predict the speech more effectively by applying the repeated pitch. In order to verify the performance of the noise reduction system proposed in this research, a simulation program using Tensorflow and Keras libraries was coded and a simulation was done. As a result of the experiment, the proposed deep learning model improves the mean square error(MSE) of 28.5% compared to using the existing adaptive filter and 17.2% compared to using the FNN(full-connected neural network) filter.


Noise reduction, Deep learning, Convolutional neural network, Neural filter

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Indonesian Journal of Electrical Engineering and Informatics (IJEEI)
ISSN 2089-3272

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This work is licensed under a Creative Commons Attribution 4.0 International License.

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