Anomaly Detection Algorithm for Acoustics Phenomena - Equipe Software/HArdware and unKnown Environment inteRactions Accéder directement au contenu
Communication Dans Un Congrès Année : 2021

Anomaly Detection Algorithm for Acoustics Phenomena

Résumé

The evolution of technologies in call centers towards communications via ethernet is at the origin of a certain number of perturbations. These perturbations can take different forms but the most important one is the acoustic phenomena. In this paper, we present an anomaly detection algorithm based on the One-Class Support Vector Machines (OC-SVM), for the detection of these acoustic phenomena. We are exploring different feature functions and seeking to find the best pairing with the OC-SVM to most effectively detect those acoustic problems that may pose a risk to consultants. Our experimental results show a good detection rate for amplitude levels equal or higher than-15 dB.
Fichier principal
Vignette du fichier
Anomaly_Detection_Algorithm_for_Acoustics_Phenomena.pdf (596.27 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-03621200 , version 1 (28-03-2022)

Identifiants

  • HAL Id : hal-03621200 , version 1

Citer

Thomas Barguil, Johann Laurent, Nicolas Bohelay, Dominique Heller. Anomaly Detection Algorithm for Acoustics Phenomena. The 2021 World Congress in Computer Science, Computer Engineering, & Applied Computing (CSCE'21), Jul 2021, Las Vegas, United States. ⟨hal-03621200⟩
48 Consultations
91 Téléchargements

Partager

Gmail Facebook X LinkedIn More