TY - JOUR
T1 - A more effective speech enhancement algorithm under non-stationary noise environment
AU - Cheng, Gong
AU - Guo, Lei
AU - Zhao, Tianyun
AU - He, Sheng
PY - 2010/10
Y1 - 2010/10
N2 - Aiming at non-stationary noise environment and low SNR (signal to noise ratio), we propose a more effective speech enhancement algorithm. The non-stationary noise estimation is based on noisy speech signal properties from low-frequency regions and high-frequency regions. Section 1 of the full paper explains what we believe to be a more effective algorithm; its core consists of: (1) we construct a time-varying weighting value for each frame of windowed speech signals through non-stationary noise estimation to realize the real time noise estimation; (2) utilizing the human auditory masking properties, we use Eq.(12) to calculate the masking threshold values of each frame of speech signals in its different BARK regions; (3) we then use the calculated masking threshold values to self-adaptively adjust the speech enhancement coefficients calculated by using Eq.(4). Section 2 simulates our speech enhancement algorithm to verify its effectiveness; the simulation results, given in Figs.2 and 3 and Table 1, show preliminarily that our algorithm is more effective for reducing background noise, improving SNR and decreasing speech distortion.
AB - Aiming at non-stationary noise environment and low SNR (signal to noise ratio), we propose a more effective speech enhancement algorithm. The non-stationary noise estimation is based on noisy speech signal properties from low-frequency regions and high-frequency regions. Section 1 of the full paper explains what we believe to be a more effective algorithm; its core consists of: (1) we construct a time-varying weighting value for each frame of windowed speech signals through non-stationary noise estimation to realize the real time noise estimation; (2) utilizing the human auditory masking properties, we use Eq.(12) to calculate the masking threshold values of each frame of speech signals in its different BARK regions; (3) we then use the calculated masking threshold values to self-adaptively adjust the speech enhancement coefficients calculated by using Eq.(4). Section 2 simulates our speech enhancement algorithm to verify its effectiveness; the simulation results, given in Figs.2 and 3 and Table 1, show preliminarily that our algorithm is more effective for reducing background noise, improving SNR and decreasing speech distortion.
KW - Auditory masking property
KW - Estimation
KW - Non-stationary noise estimation
KW - Signal to noise ratio
KW - Speech enhancement
UR - http://www.scopus.com/inward/record.url?scp=78649756725&partnerID=8YFLogxK
M3 - 文章
AN - SCOPUS:78649756725
SN - 1000-2758
VL - 28
SP - 664
EP - 668
JO - Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University
JF - Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University
IS - 5
ER -