Sciweavers

TASLP
2008

On the Importance of the Pearson Correlation Coefficient in Noise Reduction

13 years 11 months ago
On the Importance of the Pearson Correlation Coefficient in Noise Reduction
Noise reduction, which aims at estimating a clean speech from noisy observations, has attracted a considerable amount of research and engineering attention over the past few decades. In the single-channel scenario, an estimate of the clean speech can be obtained by passing the noisy signal picked up by the microphone through a linear filter/transformation. The core issue, then, is how to find an optimal filter/transformation such that, after the filtering process, the signal-to-noise ratio (SNR) is improved but the desired speech signal is not noticeably distorted. Most of the existing optimal filters (such as the Wiener filter and subspace transformation) are formulated from the mean-square error (MSE) criterion. However, with the MSE formulation, many desired properties of the optimal noise-reduction filters such as the SNR behavior cannot be seen. In this paper, we present a new criterion based on the Pearson correlation coefficient (PCC). We show that in the context of noise reduct...
Jacob Benesty, Jingdong Chen, Yiteng Huang
Added 15 Dec 2010
Updated 15 Dec 2010
Type Journal
Year 2008
Where TASLP
Authors Jacob Benesty, Jingdong Chen, Yiteng Huang
Comments (0)