Système d'identification automatique

Le Système d'identification automatique (SIA) ou Automatic Identification System (AIS) en anglais est un système d’échanges automatisés de messages entre navires par radio VHF qui permet aux navires et aux systèmes de surveillance de trafic (CROSS en France) de connaître l'identité, le statut, la position et la route des navires se situant dans la zone de navigation. Le chapitre V de la Convention SOLAS imposait que les navires de jauge brute supérieure à 300 effectuant des voyages internationaux soient équipés de ce dispositif d'ici juillet 2007 au plus tard. Pour les navires de commerce, le système doit pouvoir être interfacé à un ordinateur externe pour une éventuelle utilisation par un pilote. Fonctionnement L'AIS est composé d'un émetteur, de 2 récepteurs TDMA VHF, d'un calculateur, d'un système ASN (appel sélectif numérique), d'un système de positionnement par satellite, d'un DCU (Display Control Unit : écran de contrôle) et est interfacé avec les instruments du n

Aural and automatic forensic speaker recognition in mismatched conditions

Anil Alexander, Andrzej Drygajlo

In this article, we compare aural and automatic speaker recognition in the context of forensic analyses, using a Bayesian framework for the interpretation of evidence. We use perceptual tests performed by non-experts and compare their performance with that of an automatic speaker recognition system. These experiments are performed with 90 phonetically untrained subjects. Several forensic cases were simulated, using the Polyphone IPSC-02 database, varying in linguistic content and technical conditions of recording. We estimate the strength of evidence for both humans and the baseline automatic system, calculating likelihood ratios using perceptual scores for humans and log-likelihood scores for the automatic system. A methodology analogous to the Bayesian interpretation in forensic automatic speaker recognition is applied to the perceptual scores given by humans in order to estimate the strength of evidence. The degradation of the accuracy of human recognition in mismatched recording conditions is contrasted with that of the automatic system under similar recording conditions. The conditions considered are fixed telephone, cellular telephone and noisy speech in forensically realistic conditions. The perceptual cues that the human subjects use to perceive differences in voices are studied, along with their importance in different recording conditions. We observe that while automatic speaker recognition shows higher accuracy in matched conditions of training and testing, its performance degrades significantly in mismatched conditions. Aural recognition accuracy is also observed to degrade from matched conditions to mismatched conditions and in mismatched conditions, the baseline automatic systems showed comparable or slightly degraded performance compared to the aural recognition systems. The baseline automatic system with adaptation to noisy conditions showed comparable or better performance than aural recognition. The higher level perceptual cues used by human listeners in order to recognise speakers are discussed. We also discuss the possibility of increasing the accuracy of automatic systems using the perceptual cues that remain robust to mismatched recording conditions.

2005

Quadruplet Selection Methods For Deep Embedding Learning

Erhan Gündogdu

Recognition of objects with subtle differences has been used in many practical applications, such as car model recognition and maritime vessel identification. For discrimination of the objects in fine-grained detail, we focus on deep embedding learning by using a multi-task learning framework, in which the hierarchical labels (coarse and fine labels) of the samples are utilized both for classification and a quadruplet-based loss function. In order to improve the recognition strength of the learned features, we present a novel feature selection method specifically designed for four training samples of a quadruplet. By experiments, it is observed that the selection of very hard negative samples with relatively easy positive ones from the same coarse and fine classes significantly increases some performance metrics in a fine-grained dataset when compared to selecting the quadruplet samples randomly. The feature embedding learned by the proposed method achieves favorable performance against its state-of-the-art counterparts.

IEEE2019

Aural and automatic forensic speaker recognition in mismatched conditions

Anil Alexander, Andrzej Drygajlo

2005

Système d'identification automatique

Preventive identification of chemical hazards in laboratories

Aural and automatic forensic speaker recognition in mismatched conditions

Quadruplet Selection Methods For Deep Embedding Learning

Preventive identification of chemical hazards in laboratories

Aural and automatic forensic speaker recognition in mismatched conditions

Quadruplet Selection Methods For Deep Embedding Learning