A novel approach for design of a speech enhancement system using NLMS adaptive filter and ZCR based pattern identification

Speech signals often get degraded by various types of noise at different stages of speech recording, processing and communication systems. One major source of noise is the background noise, which highly degrades the speech signal quality and decreases the listening comfort. Speech enhancement is a section of digital speech processing in which the interfering noise is eliminated from the speech and the noise-free speech is estimated from the noisy speech signal. The work here proposes a novel approach for the enhancement of speech signal which has been highly degraded by background noise. The noisy speech signal is fed through two different stages. In the first stage, an auto-trained NLMS adaptive filter is applied to reduce the noise level. The auto trained adaptive filter automatically designs itself for the particular background without any previous training for that particular background. Then the output is passed through a ZCR based pattern identification approach for further enhancement of the speech signal. It is observed that the proposed system increases the overall output SNR of the signal by about 4 times of the input SNR.