Robust Environmental Sound Recognition with Fast Noise Suppression for Home Automation

Jia Ching Wang, Yuan Shan Lee, Chang Hong Lin, Ernestasia Siahaan, Chung Hsien Yang

Research output: Contribution to journalArticlepeer-review

14 Scopus citations


This paper proposes a robust environmental sound recognition system using a fast noise suppression approach for home automation applications. The system comprises a fast subspace-based noise suppression module and a sound classification module. For the noise suppression module, we propose a noise suppression method that applies fast subspace approximations in the wavelet domain. We show that this method offers a lower computational cost than conventional methods. In the sound classification module, we use a feature extraction method that is also based on the wavelet subspace, derived from seventeen critical bands in a signal's wavelet packet transform. Furthermore, we create a multiclass support vector machine by employing probability product kernels. The experimental results for ten classes of various environmental sounds show that the proposed system offers robust performance in environmental sound recognition tasks.

Original languageEnglish
Article number7244258
Pages (from-to)1235-1242
Number of pages8
JournalIEEE Transactions on Automation Science and Engineering
Issue number4
StatePublished - Oct 2015


  • Environmental sound recognition
  • noise suppression
  • probability product kernel
  • support vector machine
  • wavelet transform


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