Endre søk
RefereraExporteraLink to record
Permanent link

Direct link
Referera
Referensformat
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annet format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annet språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf
Classifying environmental sounds using image recognition networks
Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datalogi och datorsystemteknik.
Sony Mobile Communications AB, SWE.
Sony Mobile Communications AB, SWE.
Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datalogi och datorsystemteknik.
2017 (engelsk)Inngår i: Procedia Computer Science / [ed] Toro C.,Hicks Y.,Howlett R.J.,Zanni-Merk C.,Toro C.,Frydman C.,Jain L.C.,Jain L.C., Elsevier B.V. , 2017, Vol. 112, s. 2048-2056Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

Automatic classification of environmental sounds, such as dog barking and glass breaking, is becoming increasingly interesting, especially for mobile devices. Most mobile devices contain both cameras and microphones, and companies that develop mobile devices would like to provide functionality for classifying both videos/images and sounds. In order to reduce the development costs one would like to use the same technology for both of these classification tasks. One way of achieving this is to represent environmental sounds as images, and use an image classification neural network when classifying images as well as sounds. In this paper we consider the classification accuracy for different image representations (Spectrogram, MFCC, and CRP) of environmental sounds. We evaluate the accuracy for environmental sounds in three publicly available datasets, using two well-known convolutional deep neural networks for image recognition (AlexNet and GoogLeNet). Our experiments show that we obtain good classification accuracy for the three datasets. © 2017 The Author(s).

sted, utgiver, år, opplag, sider
Elsevier B.V. , 2017. Vol. 112, s. 2048-2056
Emneord [en]
Convolutional Neural Networks, Deep Learning, Environmental Sound Classification, GPU Processing, Image Classification, Classification (of information), Convolution, Deep neural networks, Image recognition, Knowledge based systems, Neural networks, Automatic classification, Classification accuracy, Classification tasks, Convolutional neural network, Environmental sound classifications, Environmental sounds, Image representations
HSV kategori
Identifikatorer
URN: urn:nbn:se:bth-15478DOI: 10.1016/j.procs.2017.08.250ISI: 000418466000216Scopus ID: 2-s2.0-85032359938OAI: oai:DiVA.org:bth-15478DiVA, id: diva2:1156090
Konferanse
21st International Conference on Knowledge - Based and Intelligent Information and Engineering Systems, (KES), Marseille
Tilgjengelig fra: 2017-11-10 Laget: 2017-11-10 Sist oppdatert: 2018-01-18bibliografisk kontrollert

Open Access i DiVA

Fulltekst mangler i DiVA

Andre lenker

Forlagets fulltekstScopus

Personposter BETA

Lundberg, Lars

Søk i DiVA

Av forfatter/redaktør
Boddapati, VenkateshLundberg, Lars
Av organisasjonen

Søk utenfor DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric

doi
urn-nbn
Totalt: 156 treff
RefereraExporteraLink to record
Permanent link

Direct link
Referera
Referensformat
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annet format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annet språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf