Noisy speech in real time iterative Wiener filter
Wang Jingfang · 2011
As many traditional denoising methods fail in the intensive noises environment and be unadaptable in various noisy environments, a method based on iterative Wiener filtering feature extraction was applied for acoustic signals. It frames the acoustic signals at first. Then, the iterative renewing methods are advanced in noising spectral frequency and signal-to-noise ratio (SNR) of spectral power. This method is implemented in detail. The experimental results show that the proposed algorithm can filter noise from voice effectively and improve the performance of automatic speech recognition system significantly. It is proved to be robust under various noisy environments and signal-to-noise ratio (SNR) conditions. The algorithm is of low computational complexity which is suitable for embedded automatic speech recognition system application.