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bayesian-classifiers
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A Soft Spectrographic Mask Estimation for Speech Recognition
, M.Sc. Thesis Sharif University of Technology ; Sameti, Hossein (Supervisor)
Abstract
Nowadays, robustness of the Automatic Speech Recognition (ASR) systems against various noises is major challenge in these systems. Missing feature speech recognition approaches are our goal in this thesis for achieving robust ASR systems. In these approaches, low SNR regions of a spectrogram are considered to be “missing” or “unreliable” and are removed from the spectrogram. Noise compensation is carried out by either estimating the missing regions from the remaining regions in some manner prior to recognition, or by performing recognition directly on incomplete spectrograms. These techniques clearly require a "spectrographic mask" which accurately labels the reliable and unreliable regions...
Detection of human attention using EEG signals
, Article 24th Iranian Conference on Biomedical Engineering and 2017 2nd International Iranian Conference on Biomedical Engineering, ICBME 2017, 30 November 2017 through 1 December 2017 ; 2018 ; 9781538636091 (ISBN) ; Hajipour Sardouie, S ; Sharif University of Technology
Abstract
Attention as a cognitive aspect of brain activity is one of the most popular areas of brain studies. It has significant impact on the quality of other activities such as learning process and critical activities (e.g. driving vehicles). Because of its crucial influence on the learning process, it is one of the main aspects of research in education. In this study, we propose a brand new protocol of brain signal recording in order to classify human attention in educational environments. Unlike other protocols used to record EEG signals, our protocol does not require strong memory and strong language knowledge to carry out. To this end, we have recorded EEG signals of 12 subjects using the...
A new Bayesian classifier for skin detection
, Article 3rd International Conference on Innovative Computing Information and Control, ICICIC'08, Dalian, Liaoning, 18 June 2008 through 20 June 2008 ; 2008 ; 9780769531618 (ISBN) ; Mousavi, M. E ; Sharif University of Technology
2008
Abstract
Skin detection has different applications in computer vision such as face detection, human tracking and adult content filtering. One of the major approaches in pixel based skin detection is using Bayesian classifiers. Bayesian classifiers performance is highly related to their training set. In this paper, we introduce a new Bayesian classifier skin detection method. The main contribution of this paper is creating a huge database to create color probability tables and new method for creating skin pixels data set. Our database consists of about 80000 images containing more than 5 billions pixels. Our tests shows that the performance of Bayesian classifier trained on our data set is better than...
Classifying depth of anesthesia using EEG features, a comparison
, Article 29th Annual International Conference of IEEE-EMBS, Engineering in Medicine and Biology Society, EMBC'07, Lyon, 23 August 2007 through 26 August 2007 ; 2007 , Pages 4106-4109 ; 05891019 (ISSN) ; 1424407885 (ISBN); 9781424407880 (ISBN) ; Shamsollahi, M. B ; Arefian, N. M ; Assareh, A ; Sharif University of Technology
2007
Abstract
Various EEG features have been used in depth of anesthesia (DOA) studies. The objective of this study was to And the excellent features or combination of them than can discriminate between different anesthesia states. Conducting a clinical study on 22 patients we could define 4 distinct anesthetic states: awake, moderate, general anesthesia, and isoelectric. We examined features that have been used in earlier studies using single-channel EEG signal processing method. The maximum accuracy (99.02%) achieved using approximate entropy as the feature. Some other features could well discriminate a particular state of anesthesia. We could completely classify the patterns by means of 3 features and...