, Les paramètres d'échelles sont estimés et lissés grâce à la méthode énoncée dans la section 14.2. Nous procédons alors au FWP de l'équation (14.5) fournissant deux nouvelles estimation de la parole? et du bruitn. Nous réitérons la procédure d'estimation des paramètres et de filtrage

, Algorithme 9 MAD : Algorithme MultiAlpha Denoising . 1. Entrées ? TFCT x du signal monocanal ? Nombre d'itérations ? Exposants caractéristiques ? s et ? n ? Horizons de taille fixe ? s et ? n pour le lissage du log-spectrogramme 2

, Calcul de? s et? n obtenues à partir de la distribution

, Estimation des paramètres d'échelles ? Calculer les log-spectrogrammes ln (|? ( f , t)|) et ln (|n ( f , t)|). Les lisser en considérant les paramètres ? s et ? n : il en résulte? s ( f , t)

. Dans-le-but-d'évaluer and . Mad, Ces signaux de paroles durent approximativement 3 secondes et sont échantillonnés à 8 kHz. Chacun des 30 extraits est alors corrompu par un bruit additif avec différents rapports de signal à bruit (RSB) : 0, 5, 10 et 15 dB. Trois bruits de NOIZEUS sont utilisés : le moteur d'une voiture (Voiture), un bruit de foule (Foule) et le hall d'un aéroport (Aéroport). La TFCT est faite avec des fenêtres de 1024 échantillons en employant une fenêtre de Hamming et 85% de chevauchement entre les trames, nous avons pris en compte 30 signaux de paroles extraits de la base de données NOIZEUS 7

, Les algorithmes suivants seront comparés : MAD Notre algorithme proposé dans la section 14.3. Dans cette évaluation, nous considérons ? s = 1.3 et ? n = 1.8 pour les exposants caractéristiques. Les longueurs des filtres de lissage sont de ? s = 0.09 s et ? n = 0.16 s pour l'estimation des paramètres d'échelles. Ces longueurs signifient que le bruit est supposé plus stationnaire que la parole. Nous itérons le procédé d'estimation de paramètre et de filtrage 4 fois. Comme mentionné dans l'algorithme 9, nous initialisons la parole et le bruit en posant? =n = x/2. SSA L'algorithme de soustraction spectrale d' amplitude (SSA) est le cas particulier où ? = 1 dans (13.10). Nous utilisons un détecteur d'activité vocale (DAV) dit « parfait », dans le sens où nous l'appliquons sur la parole non bruitée. SSG La soustraction spectrale généralisée (SSG) est celle décrite dans l'équation (13.11). À nouveau, un DAV « parfait

, 3 -Capture d'écran : Version de MAD en ligne pour la restauration d'archives du CREM, NOIZEUS

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