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Lecture Notes in Computer Science, 2013
The topic of this thesis work is soft computing based feature selection for environmental sound c... more The topic of this thesis work is soft computing based feature selection for environmental sound classification. Environmental sound classification systems have a wide range of applications, like hearing aids devices, handheld devices and auditory protection devices. Sound classification systems typically extract features which are learnt by a classifier. Using too many features can result in reduced performance by making the learning algorithm to learn wrong models. The proper selection of features for sound classification is a non-trivial task. Soft computing based feature selection methods are not studied for environmental sound classification, whereas these methods are very promising, because these can handle uncertain information in a more efficient way, using simple set theoretic functions and because these methods are more close to perception based reasoning. Therefore this thesis investigates different feature selection methods, including soft computing based feature selection and classical information, entropy and correlation based approaches. Results of this study show that rough set neighborhood based method performs best in terms of number of features selected, recognition rate and consistency of performance. Also the resulting classification system performs robustly in presence of reverberation.
Lecture Notes in Computer Science, 2013
The processing flow in the FBK ASR system ◮ Brief description of some acoustic modeling technique... more The processing flow in the FBK ASR system ◮ Brief description of some acoustic modeling techniques ◮ Brief description of some LM representation techniques ◮ Models used for Evalita evaluation ◮ Results
Lecture Notes in Computer Science, 2013
Rome, 25-1-2012 1 R.Ronny, now Master student ad Edinburgh, trained the system during his summer ... more Rome, 25-1-2012 1 R.Ronny, now Master student ad Edinburgh, trained the system during his summer student internship at FBK. Outline ◮ The processing flow in the FBK ASR system ◮ Brief description of some acoustic modeling techniques ◮ Brief description of some LM representation techniques ◮ Models used for Evalita evaluation ◮ Results
Lecture Notes in Computer Science, 2013
The topic of this thesis work is soft computing based feature selection for environmental sound c... more The topic of this thesis work is soft computing based feature selection for environmental sound classification. Environmental sound classification systems have a wide range of applications, like hearing aids devices, handheld devices and auditory protection devices. Sound classification systems typically extract features which are learnt by a classifier. Using too many features can result in reduced performance by making the learning algorithm to learn wrong models. The proper selection of features for sound classification is a non-trivial task. Soft computing based feature selection methods are not studied for environmental sound classification, whereas these methods are very promising, because these can handle uncertain information in a more efficient way, using simple set theoretic functions and because these methods are more close to perception based reasoning. Therefore this thesis investigates different feature selection methods, including soft computing based feature selection and classical information, entropy and correlation based approaches. Results of this study show that rough set neighborhood based method performs best in terms of number of features selected, recognition rate and consistency of performance. Also the resulting classification system performs robustly in presence of reverberation.
Lecture Notes in Computer Science, 2013
The processing flow in the FBK ASR system ◮ Brief description of some acoustic modeling technique... more The processing flow in the FBK ASR system ◮ Brief description of some acoustic modeling techniques ◮ Brief description of some LM representation techniques ◮ Models used for Evalita evaluation ◮ Results
Lecture Notes in Computer Science, 2013
Rome, 25-1-2012 1 R.Ronny, now Master student ad Edinburgh, trained the system during his summer ... more Rome, 25-1-2012 1 R.Ronny, now Master student ad Edinburgh, trained the system during his summer student internship at FBK. Outline ◮ The processing flow in the FBK ASR system ◮ Brief description of some acoustic modeling techniques ◮ Brief description of some LM representation techniques ◮ Models used for Evalita evaluation ◮ Results