ISCA Archive ICSLP 2000
ISCA Archive ICSLP 2000

Dominant subspace analysis for auditory spectrum

Xugang Lu, Gang Li, Lipo Wang

In hearing perception theory, spectral structure is a most important feature for speech perception, this spectral structure is not easy to be masked in noisy condition. So if this structure is extracted and enhanced, the representation will be much more robust. In this paper, we propose a new statistical dominant subspace analysis method for auditory spectrum based on SVD(Singular Values Decomposition) and signal subspace analysis method. The auditory spectrum can be decomposed into two subspaces, one is a dominant subspace, which is expanded by useful speech auditory spectrum , another subspace is sub-dominant subspace, which there is only noise information. So we analyze the auditory spectrum in the dominant subspace, the SNR will be increased. Thus this representation is much more robust.


Cite as: Lu, X., Li, G., Wang, L. (2000) Dominant subspace analysis for auditory spectrum. Proc. 6th International Conference on Spoken Language Processing (ICSLP 2000), vol. 3, 59-62

@inproceedings{lu00b_icslp,
  author={Xugang Lu and Gang Li and Lipo Wang},
  title={{Dominant subspace analysis for auditory spectrum}},
  year=2000,
  booktitle={Proc. 6th International Conference on Spoken Language Processing (ICSLP 2000)},
  pages={vol. 3, 59-62}
}