Abstract
In this paper, residual noise of corrupted speech observations is further restrained based on eigencomponent (an eigenvalue and its corresponding eigenvector) filtering. Three relevant algorithms are proposed to obtain the core eigencomponents that deeply affect enhancement quality of speech fragments by joint diagonalization of clean speech and noise covariance matrix. In addition, the generalized inverse matrix transform is introduced to the recovery of enhanced speech signal for the issue of matrix irreversibility after eigencomponents are filtered. Experiment results show that the proposed methods work better than many other methods under various conditions on both noise reduction and speech distortion.
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Huang, K., Liu, Y. & Hong, Y. Reduction of residual noise based on eigencomponent filtering for speech enhancement. Int J Speech Technol 21, 877–886 (2018). https://doi.org/10.1007/s10772-018-09560-y
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DOI: https://doi.org/10.1007/s10772-018-09560-y