第22回ロボット聴覚特集 - 奥乃研究室 - 京都大学

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Missing Feature Theory, MFT[7,8] 2 MissingFeature Mask, MFM [9]8 MFM 2 MFT [7]MFT 1. Mel-Frequency Cepstral Coef- ficient,MFCCMFT 2. MFM MFM MFM MFM 3. MFT CASAToolkit (CTK) [10] MFT MFT 3 3 MFT MFCC [11]MFCC MFCC post-filter 2 MFM MFCC (1) 16 16 kHz 25 ms 10 ms FFT (2) 24 (3) 24 (4) (5) 0, 13-23 0 (6) Cepstral Mean Subtraction,CMS(7) (8) (9) 48 4 MFM 102
a) 90 b) 90 MFMc) d) MFMe) 90 f) 90 MFMFigure 2: MFM X n (k,l)GeometricsourceseparationStationarynoiseestimationY m (k,l)Interferenceleakestimationλ m leak (k,l)+λ m stat. (k,l)AttenuationruleSNR & speechprobatilityestimationλ m (k,l)Figure 1: post-filter S^ m (k,l) post-filter post-filter [12]GeometricSource Separation, GSS post-filter [13]Figure 1GSS GSS Figure 1 X n (k, i) n GSS Y m (k, i) GSS m Ŝm(k, i) post-filter m G m (k, i) Ŝm(k, i) =G m (k, i)Y m (k, i)MFM i =1, ···, N 2 M(k, i) k post-filter Y (k, i). Ŝ(k, i) post-filter N(k, i) 2 1 0 T 0.25 { )1,(Ŝ(k, i)+N(k, i) /Y (k, i) >TM(k, i) =0, otherwiseMFM i = N 2 +103
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第22回 ロボット聴覚特集 - 奥乃研究室 - 京都大学

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第22回ロボット聴覚特集 - 奥乃研究室 - 京都大学