A Signal Subspace Speech Enhancement Method for Various Noises

In this paper, we propose a single-channel speech enhancement method in terms of subspace techniques to reduce the noises from speech signals in various noises environment. This subspace approach based on Karhunen-Loève transform and implemented via Principal component analysis. The optimal subspace selection is provided by a minimum description length criterion. An offset factor generated from the white noise was used to modify the variance to adapt to the specified colored noise. Several objective speech quality measures have been introduced to give an overall evaluation of the proposed method. A large amount of data and Figures, as well as the audio quality evaluation results, testify that the algorithm provides high performance for the input signal-to-noise ratio range from -5dB to 10dB. It is showed that the proposed approach have excellent characteristics for colored noises in strong background noise environment with lower signal-to-noise ratio.