Transient noise reduction in speech signal with a modified long-term predictor
Min-Seok Choi, Hong-Goo Kang · EURASIP Journal on Advances in Signal Processing · 2011
This article proposes an efficient median filter based algorithm to remove transient noise in a speech signal. The proposed algorithm adopts a modified long-term predictor (LTP) as the pre-processor of the noise reduction process to reduce speech distortion caused by the nonlinear nature of the median filter. This article shows that the LTP analysis does not modify to the characteristic of transient noise during the speech modeling process. Oppositely, if a short-term linear prediction (STP) filter is employed as a pre-processor, the enhanced output includes residual noise because the STP analysis and synthesis process keeps and restores transient noise components. To minimize residual noise and speech distortion after the transient noise reduction, a modified LTP method is proposed which estimates the characteristic of speech more accurately. By ignoring transient noise presence regions in the pitch lag detection step, the modified LTP successfully avoids being affected by transient noise. A backward pitch prediction algorithm is also adopted to reduce speech distortion in the onset regions. Experimental results verify that the proposed system efficiently eliminates transient noise while preserving desired speech signal.