Title :
Robust Environmental Sound Recognition for Home Automation
Author :
Wang, Jia-Ching ; Lee, Hsiao-Ping ; Wang, Jhing-Fa ; Lin, Cai-Bei
Author_Institution :
Nat. Cheng Kung Univ., Tainan
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 independent 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.
Keywords :
audio signal processing; cepstral analysis; frame based representation; home automation; independent component analysis; signal classification; support vector machines; wavelet transforms; Mel-frequency cepstral coefficients; environmental sound recognition; frame-based multiclass support vector machines; home automation; human intention detection; human speech; independent component analysis; signal-to-noise ratio-aware subspace-based signal enhancement; sound category; sound class identification; speech recognition; wavelet transform; Acoustic noise; Automatic speech recognition; Cepstral analysis; Home automation; Humans; Independent component analysis; Monitoring; Robustness; Support vector machine classification; Support vector machines; Home automation; independent component analysis (ICA); mel-frequency cepstral coefficients (MFCCs); signal enhancement; sound recognition; support vector machines (SVMs); wavelet transform;
Journal_Title :
Automation Science and Engineering, IEEE Transactions on
DOI :
10.1109/TASE.2007.911680
