lnu.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
IoT-based Urban Noise Identification Using Machine Learning: Performance of SVM, KNN, Bagging, and Random Forest
Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM).
Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM). (Parallel Computing)ORCID iD: 0000-0002-4146-9062
Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM).ORCID iD: 0000-0003-0512-6350
2019 (English)In: Proceedings of the International Conference on Omni-Layer Intelligent Systems (COINS '19), New York: ACM Publications, 2019, p. 62-67Conference paper, Published paper (Refereed)
Abstract [en]

Noise is any undesired environmental sound. A sound at the same dB level may be perceived as annoying noise or as pleasant music. Therefore, it is necessary to go beyond the state-of-the-art approaches that measure only the dB level and also identify the type of noise. In this paper, we present a machine learning based method for urban noise identification using an inexpensive IoT unit. We use Mel-frequency cepstral coefficients for audio feature extraction and supervised classification algorithms (that is, support vector machine, k-nearest neighbors, bootstrap aggregation, and random forest) for noise classification. We evaluate our approach experimentally with a data-set of about 3000 sound samples grouped in eight sound classes (such as car horn, jackhammer, or street music). We explore the parameter space of the four algorithms to estimate the optimal parameter values for classification of sound samples in the data-set under study. We achieve a noise classification accuracy in the range 88% - 94%.

Place, publisher, year, edition, pages
New York: ACM Publications, 2019. p. 62-67
Keywords [en]
bootstrap aggregation (Bagging), internet of things (IoT), k-nearest neighbors (KNN), mel-frequency cepstral coefficients (MFCC), random forest, smart cities, support vector machine (SVM), urban noise
National Category
Computer Systems
Research subject
Computer Science, Software Technology
Identifiers
URN: urn:nbn:se:lnu:diva-81767DOI: 10.1145/3312614.3312631Scopus ID: 2-s2.0-85066804134ISBN: 978-1-4503-6640-3 (print)OAI: oai:DiVA.org:lnu-81767DiVA, id: diva2:1303311
Conference
International Conference on Omni-Layer Intelligent Systems (COINS '19), Crete, Greece — May 05 - 07, 2019
Funder
Knowledge Foundation, 20150088, 20150259Available from: 2019-04-09 Created: 2019-04-09 Last updated: 2019-08-29Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full textScopus

Authority records BETA

Pllana, SabriKurti, Arianit

Search in DiVA

By author/editor
Alsouda, YasserPllana, SabriKurti, Arianit
By organisation
Department of computer science and media technology (CM)
Computer Systems

Search outside of DiVA

GoogleGoogle Scholar

doi
isbn
urn-nbn

Altmetric score

doi
isbn
urn-nbn
Total: 120 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf