DEVELOPMENT OF AN ADAPTIVE NOISE REDUCTION SYSTEM WITH AUTOMATIC WIND NOISE DETECTION UTILIZING TMS320C6713

摘要

The purpose of this study was to develop an adaptive wind noise reduction system. Our system has two parts: firstly we applied the decision tree machine learning algorithm to detect existence of wind noise with the mel frequency cepstrum coefficients (MFCC) used as input features, and parameters of an adaptive filter would be changed to reduce the wind noise. Then we calculated the input short time entropy to detect the voice activity in order to make the output speech signal more comfortable and intelligible. This approach would reduce the wind noise if it detected the input signals with no speech activity. To verify if our system could reduce different wind noise properly, we applied real and simulated wind noise as the noise sources with SNR set from 10 to -10dB, and compared our results with two common noise reduction algorithms: minima controlled recursive averaging (MCRA) and Forward-Backward MCRA (MCRA-FB). Then the objective perceptual evaluation of speech quality (PESQ) approach was used to evaluate the quality of the results. In this study, the MATLAB program was first used to implement the wind noise reduction system. Our results showed that the PESQ score was increased by 0.35 when compared to the original signal with 0dB SNR real wind noise signal while MCRA-FB algorithm could only be increased by 0.05. At the same time, the speech hit rate was 96%, and the accuracy of the wind noise detection rate is 93%. We further implemented the wind noise reduction system on the DSP starter kit (DSK), TMS320C6713 and compared to the results of MCRA. Our results indicated the PESQ score could be increased by 0.3 at high SNR (6dB) signal while the results of MCRA algorithm could not improve the PESQ score. These results show that our wind noise reduction system achieves better performance.