Robust environmental sound recognition for home automation

Jia Ching Wang, Hsiao Ping Lee, Jhing Fa Wang, Cai Bei Lin

Research output: Contribution to journalArticlepeer-review

101 Scopus citations

Abstract

This work presents a robust environmental sound recognition system for home automation. Specific home automation services can be activated based on identified sound classes. Additionally, when the sound category is human speech, such speech can be recognized for detecting human intentions as in conventional research on home automation. To attain this ambitious goal, this study uses two key techniques: signal-to-noise ratio-aware subspace-based signal enhancement and sound recognition with in-dependent component analysis mel-frequency cepstral coefficients and a frame-based multiclass support vector machines, respectively. Simulations and an experiment in a real-world environment are given to illustrate the performance of the proposed robust sound recognition system.

Original languageEnglish
Pages (from-to)25-31
Number of pages7
JournalIEEE Transactions on Automation Science and Engineering
Volume5
Issue number1
DOIs
StatePublished - Jan 2008

Keywords

  • Home automation
  • Independent component analysis (ICA)
  • Mel-frequency cepstral coefficients (MFCCs)
  • Signal enhancement
  • Sound recognition
  • Support vector machines (SVMs)
  • Wavelet transform

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