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Download An Introduction to Modern Solid State Physics by Yuri M. Galperin PDF

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By Yuri M. Galperin

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75) = I M are also diagonal matrices. 78) is the residual noise. 80) = H H H. 74) as the signal model for noise reduction, we find that the input SNR is iSNR = tr x tr v tr = . 83) and, clearly, the noise reduction factor is ξnr H = tr v tr vrn M = . 85) 43 General Concept with the Joint Diagonalization of the Speech and Noise Correlation Matrices and υsd H = We also have H − IM tr H − IM H . 86) . 89) ers = H v . 92) and Jrs H = tr H H H . 94) Hmax = ⎢ . . ⎥ . ⎣ .. . .. 97) ≤ 1. 98) ≤ M. , υsd Hmax = 0] and gives the maximum gain in SNR (equal to M).

73) where Rxs = E xs (k)xsT (k) = diag λx ,1 , λx ,2 , . . , λx ,P = xs . 78) Single-Channel Speech Enhancement in the Time Domain 59 is the residual noise. 79) H = Tx H. 83) Rvrn = HRv H . 24). 65), since we consider xi (k) as an interference and x (k) as the desired signal vector. We obtain iSNR = tr Rx tr Rin . 85) The output SNR and the noise reduction factor are, respectively, oSNR H = = tr Rxfd tr Rxri + Rvrn and ξnr H = 2 Eventually, T T xs Tx H HRin H T tr HTx tr tr Rin tr HRin H T only the first P components of z (k) are relevant.

78) is the residual noise. 80) = H H H. 74) as the signal model for noise reduction, we find that the input SNR is iSNR = tr x tr v tr = . 83) and, clearly, the noise reduction factor is ξnr H = tr v tr vrn M = . 85) 43 General Concept with the Joint Diagonalization of the Speech and Noise Correlation Matrices and υsd H = We also have H − IM tr H − IM H . 86) . 89) ers = H v . 92) and Jrs H = tr H H H . 94) Hmax = ⎢ . . ⎥ . ⎣ .. . .. 97) ≤ 1. 98) ≤ M. , υsd Hmax = 0] and gives the maximum gain in SNR (equal to M).

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