Abstract
Voice denoising is the process of removing undesirable voices from the voice signal. Within the environmental noise and after the application of speech recognition system, the discriminative model finds it difficult to recognize the waveform of the voice signal. This is due to the fact that the environmental noise needs to use a suitable filter that does not affect the shaped waveform of the input microphone. This paper plans to build up a procedure for a discriminative model, using infinite impulse response filter (Butterworth filter) and local polynomial approximation (Savitzky-Golay) smoothing filter that is a polynomial regression on the signal values. Signal to noise ratio (SNR) was calculated after filtering to compare the results after and before adding the Savitzky-Golay smoothing filter. This procedure showed better results for the filtering of ambient noise and protecting a waveform from distortion, which makes the discriminative model more accurate when recognizing voice. Our procedure for preprocessing was developed and successfully implemented on a discriminative model by using MATLAB.
Highlights
Voice enhancement is the process of improving the quality of the voice signal by lessening the foundation loud noise and other undesirable sounds
The results showed that the Signal to noise ratio (SNR) was increased from -3.4745 to 0.0497, making the discriminative model more accurate
After developing the entire system, a testing was made for the proposed procedure based on the preprocessing, which is the important step in the discriminative model
Summary
Voice enhancement is the process of improving the quality of the voice signal by lessening the foundation loud noise and other undesirable sounds. Lee et al [5] proposed a procedure based on a system that recognizes the content of speech that has ambient noise. The input microphone is passed through a passband filter for the cut-off between high and low frequencies.
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