Abstract
In this paper, we studied the effect of the optimization algorithm of weight coefficients on the performance of the CNN(Convolutional Neural Network) noise attenuator. This system improves the performance of the noise attenuation by a deep learning algorithm using the neural network adaptive predictive filter instead of using the existing adaptive filter. Speech is estimated from a single input speech signal containing noise using 64-neuron, 16-filter CNN filters and an error back propagation algorithm. This is to use the quasi-periodic nature of the voiced sound section of the voice signal. In this study, to verify the performance of the noise attenuator for the optimization, a test program using the Keras library was written and training was performed. As a result of simulation, this system showed the smallest MSE value when using the Adam algorithm among the Adam, RMSprop, and Adagrad optimization algorithms, and the largest MSE value in the Adagrad algorithm. This is because the Adam algorithm requires a lot of computation but it has an exellent ability to estimate the optimal value by using the advantages of RMSprop and Momentum SGD.
Highlights
Noise attenuation is to attenuate noise included in speech, and various studies have been conducted on noise attenuation technology so far
These methods subtract the spectrum of noise estimated from the input speech signal or estimate the clear speech spectrum, and are advantageous when the noise and statistical characteristics of the speech signal are known in advance
We propose the best performing algorithm by examining the effect of the optimization algorithm on the performance when noise is attenuated using the deep learning algorithm of the CNN neural network filter instead of the adaptive filter of the adaptive noise attenuator
Summary
Noise attenuation is to attenuate noise included in speech, and various studies have been conducted on noise attenuation technology so far. In 2016, a model based on SNR (Signal to Noise Ratio)-aware CNN for speech enhancement was published [11]. We propose the best performing algorithm by examining the effect of the optimization algorithm on the performance when noise is attenuated using the deep learning algorithm of the CNN neural network filter instead of the adaptive filter of the adaptive noise attenuator.
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