Robust Environmental Sound Recognition With Fast Noise Suppression for Home Automation

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 su...

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Bibliographic Details
Published in:IEEE transactions on automation science and engineering 2015-10, Vol.12 (4), p.1235-1242
Main Authors: Jia-Ching Wang, Yuan-Shan Lee, Chang-Hong Lin, Siahaan, Ernestasia, Chung-Hsien Yang
Format: Article
Language:English
Subjects:
Artificial intelligence
Automation
Background noise
Building automation
Classification
Computational complexity
Environmental sound recognition
Feature extraction
Kernel
Modules
Noise
Noise control
Noise measurement
Noise reduction
noise suppression
probability product kernel
Recognition
Smart houses
Sound
support vector machine
Support vector machines
Voice recognition
Wavelet
wavelet transform
Wavelet transforms
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Summary: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.
ISSN:1545-5955
1558-3783
DOI:10.1109/TASE.2015.2470119