tailieunhanh - Báo cáo hóa học: " A Block-Based Linear MMSE Noise Reduction with a High Temporal Resolution Modeling of the Speech Excitation Chunjian Li"

Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: A Block-Based Linear MMSE Noise Reduction with a High Temporal Resolution Modeling of the Speech Excitation Chunjian Li | EURASIP Journal on Applied Signal Processing 2005 18 2965-2978 2005 C. Li and S. V. Andersen A Block-Based Linear MMSE Noise Reduction with a High Temporal Resolution Modeling of the Speech Excitation Chunjian Li Department of Communication Technology Aalborg University 9220 Aalborg 0 Denmark Email cl@ S0ren Vang Andersen Department of Communication Technology Aalborg University 9220 Aalborg 0 Denmark Email sva@ Received 14 May 2004 Revised 11 March 2005 A comprehensive linear minimum mean squared error LMMSE approach for parametric speech enhancement is developed. The proposed algorithms aim at joint LMMSE estimation of signal power spectra and phase spectra as well as exploitation of correlation between spectral components. The major cause of this interfrequency correlation is shown to be the prominent temporal power localization in the excitation of voiced speech. LMMSE estimators in time domain and frequency domain are first formulated. To obtain the joint estimator we model the spectral signal covariance matrix as a full covariance matrix instead of a diagonal covariance matrix as is the case in the Wiener filter derived under the quasi-stationarity assumption. To accomplish this we decompose the signal covariance matrix into a synthesis filter matrix and an excitation matrix. The synthesis filter matrix is built from estimates of the all-pole model coefficients and the excitation matrix is built from estimates of the instantaneous power of the excitation sequence. A decision-directed power spectral subtraction method and a modified multipulse linear predictive coding MPLPC method are used in these estimations respectively. The spectral domain formulation of the LMMSE estimator reveals important insight in interfrequency correlations. This is exploited to significantly reduce computational complexity of the estimator. For resource-limited applications such as hearing aids the performance-to-complexity trade-off can be conveniently adjusted