Treffer: Privacy aware acoustic scene synthesis using deep spectral feature inversion
Title:
Privacy aware acoustic scene synthesis using deep spectral feature inversion
Contributors:
Laboratoire des Sciences du Numérique de Nantes (LS2N), Université de Nantes - UFR des Sciences et des Techniques (UN UFR ST), Université de Nantes (UN)-Université de Nantes (UN)-École Centrale de Nantes (ECN)-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom Paris (IMT)-Institut Mines-Télécom Paris (IMT), Equipes Traitement de l'Information et Systèmes (ETIS - UMR 8051), Ecole Nationale Supérieure de l'Electronique et de ses Applications (ENSEA)-Centre National de la Recherche Scientifique (CNRS)-CY Cergy Paris Université (CY), Neurocybernétique, Ecole Nationale Supérieure de l'Electronique et de ses Applications (ENSEA)-Centre National de la Recherche Scientifique (CNRS)-CY Cergy Paris Université (CY)-Ecole Nationale Supérieure de l'Electronique et de ses Applications (ENSEA)-Centre National de la Recherche Scientifique (CNRS)-CY Cergy Paris Université (CY), Institut de Recherche en Communications et en Cybernétique de Nantes (IRCCyN), Mines Nantes (Mines Nantes)-École Centrale de Nantes (ECN)-Ecole Polytechnique de l'Université de Nantes (EPUN), Université de Nantes (UN)-Université de Nantes (UN)-PRES Université Nantes Angers Le Mans (UNAM)-Centre National de la Recherche Scientifique (CNRS), ANR-16-CE22-0012,CENSE,Caractérisation des environnements sonores urbains : vers une approche globale associant données libres, mesures et modélisations(2016)
Source:
IEEE ICASSP ; https://hal.science/hal-02478866 ; IEEE ICASSP, May 2020, Barcelona, Spain. ⟨10.1109/ICASSP40776.2020.9053172⟩
Publisher Information:
CCSD
Publication Year:
2020
Subject Terms:
spectral feature inversion, privacy aware audio synthesis, deep neural network, environmental audio processing, [STAT.ML]Statistics [stat]/Machine Learning [stat.ML], [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], [INFO.INFO-MM]Computer Science [cs]/Multimedia [cs.MM], [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing
Subject Geographic:
Time:
Barcelona, Spain
Document Type:
Konferenz
conference object
Language:
English
DOI:
10.1109/ICASSP40776.2020.9053172
Availability:
Rights:
info:eu-repo/semantics/OpenAccess
Accession Number:
edsbas.AB0483D2
Database:
BASE
Weitere Informationen
International audience ; Gathering information about the acoustic environment of urban areas is now possible and studied in many major cities in the world. Part of the research is to find ways to inform the citizen about its sound environment while ensuring her privacy. We study in this paper how this application can be cast into a feature inversion problem. We argue that considering deep learning techniques to solve this problem allows us to produce sound sketches that are representative and privacy aware. Experiments done considering the dcase2017 dataset shows that the proposed learning based approach achieves state of the art performance when compared to blind inversion approaches.