INTERSPEECH 2004 - ICSLP
In previous studies, the signal subspace technique for speech enhancement was extended and a perceptually constrained generalized singular value decomposition (PCGSVD)-based algorithm  was developed which properly integrated the auditory masking effect and the GSVD algorithm. Both objective measures and subjective tests verified that this approach can offer better performance than the GSVD-based approach and the conventional spectral subtraction (SS) algorithm. But very high computational complexity is required in the PCGSVD-based method when performing the matrices decomposition via the GSVD algoruthm. In this paper, we properly utilize the time-shift property of DFT and the special structure of Hankel matrices to perform similar functions previously offered by GSVD, and a perceptually constrained minimum variance estimation algorithm is developed. By replacing GSVD algorithm with DFT, the computation complexity is significantly reduced, almost the same as the conventional SS algorithm. Experiments showed that comparable performance to that of the PCGSVD-based approach can be achieved, regardless of whether the additive noise is stationary or not, specially when it is non-white.
Bibliographic reference. Ju, Gwo-hwa / Lee, Lin-shan (2004): "Improved speech enhancement by applying time-shift property of DFT on hankel matrices for signal subspace decomposition", In INTERSPEECH-2004, 2681-2684.