Audio-based Event Recognition System for Smart Homes

Anastasios Vafeiadis, Konstantinos Votis, Dimitrios Giakoumis, Dimitrios Tzovaras, Liming Chen, Raouf Hamzaoui

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Citations (Scopus)

Abstract

Building an acoustic-based event recognition system for smart homes is a challenging task due to the lack of high-level structures in environmental sounds. In particular, the selection of effective features is still an open problem. We make an important step toward this goal by showing that the combination of Mel-Frequency Cepstral Coefficients, Zero-Crossing Rate, and Discrete Wavelet Transform features can achieve an F1 score of 96.5% and a recognition accuracy of 97.8% with a gradient boosting classifier for ambient sounds recorded in a kitchen environment.
Original languageEnglish
Title of host publicationAudio-based event recognition system for smart homes
Place of PublicationSan Francisco, CA, USA
PublisherIEEE Xplore
Pages1-8
Number of pages8
ISBN (Electronic)978-1-5386-0435-9
ISBN (Print)978-1-5386-1591-1
DOIs
Publication statusPublished - 8 Aug 2017
Event2017 IEEE Smart World, Ubiquitous Intelligence & Computing, Advanced & Trusted Computed, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation - San Francisco, CA
Duration: 4 Aug 20178 Aug 2017

Conference

Conference2017 IEEE Smart World, Ubiquitous Intelligence & Computing, Advanced & Trusted Computed, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation
Period4/08/178/08/17

Keywords

  • feature extraction
  • mel frequency cepstral coefficient
  • discrete wavelet tranforms
  • home computing
  • signal classificatioin
  • smart homes
  • assisted living
  • activity recognition
  • audio feature extraction
  • classification
  • mel-frequency
  • zero-crossing rate
  • wavelets

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