Performance of nonlinear speech enhancement using phase space reconstruction

This paper presents the implementation of two nonlinear noise reduction methods applied to speech enhancement. The methods are based on embedding the noisy signal in a high-dimensional reconstructed phase space and applying singular value decomposition to project the signal into a lower dimension. The advantages of these nonlinear methods include that they do not require explicit models of noise spectra and do not have the typical "musical tone" side effects associated with traditional linear speech enhancement methods. The proposed nonlinear methods are compared with traditional speech enhancement techniques, including spectral subtraction, Wiener filtering, and Ephraim-Malah filtering, on example speech utterances with additive white noise for a variety of SNR levels. The results show that the local nonlinear noise reduction method outperforms Wiener filtering and spectral subtraction but not Ephraim-Malah filtering, as had been suggested by previous studies.