Robust Environmental Sound Recognition for Home Automation

@article{citeulike:2314066, abstract = {<para> 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. </para>}, author = {Wang, J. C. and Lee, H. P. and Wang, J. F. and Lin, C. B. }, booktitle = {Automation Science and Engineering, IEEE Transactions on [see also Robotics and Automation, IEEE Transactions on]}, citeulike-article-id = {2314066}, doi = {10.1109/TASE.2007.911680}, journal = {Automation Science and Engineering, IEEE Transactions on [see also Robotics and Automation, IEEE Transactions on]}, keywords = {control, environmental, recognition, speech}, number = {1}, pages = {25--31}, posted-at = {2008-01-31 13:20:27}, priority = {2}, title = {Robust Environmental Sound Recognition for Home Automation}, url = {http://dx.doi.org/10.1109/TASE.2007.911680}, volume = {5}, year = {2008} }

TY - JOUR ID - citeulike:2314066 L3 - citeulike-article-id:2314066 TI - Robust Environmental Sound Recognition for Home Automation T2 - Automation Science and Engineering, IEEE Transactions on [see also Robotics and Automation, IEEE Transactions on] JF - Automation Science and Engineering, IEEE Transactions on [see also Robotics and Automation, IEEE Transactions on] VL - 5 IS - 1 SP - 25 EP - 31 N2 - <para> 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. </para> KW - control KW - environmental KW - recognition KW - speech AU - Wang, JC AU - Lee, HP AU - Wang, JF AU - Lin, CB PY - 2008/// UR - http://dx.doi.org/10.1109/TASE.2007.911680 ER -