In communication system environment speech signal is corrupted due to presence of additive acoustic noise, so with this distortion the effective communication is degraded in terms of the quality and intelligibility of speech. Now present research is going how effectively acoustic noise can be eliminated without affecting the original speech quality, this tends to be our challenging in this current research thesis work. Here this work proposes multi-tiered detection method that is based on time-frequency analysis (i.e. filter banks concept) of the noisy speech signals, by using standard speech enhancement method based on the proven spectral subtraction, for single channel speech data and for a wide range of noise types at various noise levels. There were various variants have been introduced to standard spectral subtraction proposed by S.F.Boll. In this thesis we designed and implemented a novel approach of Spectral Subtraction based on Minimum Statistics [MinSSS]. This means that the power spectrum of the non-stationary noise signal is estimated by finding the minimum values of a smoothed power spectrum of the noisy speech signal and thus circumvents the speech activity detection problem. This approach is also capable of dealing with non-stationary noise signals. In order to analyze the system in time frequency domain, we have implemented two different filter bank approaches such as Weighted OverLap Added (WOLA) and Fast Fourier Transform Modulated (FFTMod). The proposed systems were implemented and evaluated offline using simulation tool Matlab and then validated their performances based on the objective quality measures such as Signal to Noise Ratio Improvement (SNRI) and Perceptual Evaluation Speech Quality (PESQ) measure. The systems were tested with a pure speech combination of male and female sampled at 8 kHz, these signals were corrupted with various kinds of noises at different noise power levels. The MinSSS algorithm implemented using FFTMod filter bank approach outperforms when compared the WOLA filter bank approach.

The combination of a video camera with an acoustic vector sensor (AVS) opens new possibilities in environment awareness applications. The goal of this thesis is the design of an algorithm for detection and tracking of low-flying aircraft using a multi-coordinate VAUDEO system. A commercial webcam placed in line with an AVS in a ground array are used to record real low-flying aircraft data at Teuge international airport. Each frame, the algorithm analyzes a matrix of three orthogonal acoustic particle velocity signals and one acoustic pressure signal using the Singular Value Decomposition to estimate the Direction of Arrival, DoA of propeller aircraft sound. The DoA data is then applied to a Kalman filter and its output is used later on to narrow the region of video frame processed. Background subtraction is applied followed by a Gaussian-weighted intensity mask to assign high priority to moving objects which are closer to the sound source estimated position. The output is applied to another Kalman filter to improve the accuracy of the aircraft location estimation. The performance evaluation of the algorithm proved that it is comparable to the performances of state-of-the-art video alone based algorithms. In conclusion, the combination of video and directional audio increases the accuracy of propeller aircraft detection and tracking comparing to reported previous work using audio alone. / +593 980826278

Lie-detection has been an evergreen and evolving subject. Polygraph techniques have been the most popular and successful technique till date. The main drawback of the polygraph is that good results cannot be attained without maintaining a physical contact, of the subject under test. In general, this physical contact would induce extra consciousness in the subject. Also, any sort of arousal in the subject triggers false positives while performing the traditional polygraph based tests. With all these drawbacks in the polygraph, also, due to rapid developments in the fields of computer vision and artificial intelligence, with newer and faster algorithms, have compelled mankind to search and adapt to contemporary methods in lie-detection. Observing the facial expressions of emotions in a person without any physical contact and implementing these techniques using artificial intelligence is one such method. The concept of magnifying a micro expression and trying to decipher them is rather premature at this stage but would evolve in future. Magnification using EVM technique has been proposed recently and it is rather new to extract these micro expressions from magnified EVM based on HOG features. Till date, HOG features have been used in conjunction with SVM, and generally for person/pedestrian detection. A newer, simpler and contemporary method of applying EVM with HOG features and Back-propagation Neural Network jointly has been introduced and proposed to extract and decipher the micro-expressions on the face. Micro-expressions go unnoticed due to its involuntary nature, but EVM is used to magnify them and makes them noticeable. Emotions behind the micro-expressions are extracted and recognized using the HOG features \& Back-Propagation Neural Network. One of the important aspects that has to be dealt with human beings is a biased mind. Since, an investigator is also a human and, he too, has to deal with his own assumptions and emotions, a Neural Network is used to give the investigator an unbiased start in identifying the true emotions behind every micro-expression. On the whole, this proposed system is not a lie-detector, but helps in detecting the emotions of the subject under test. By further investigation, a lie can be detected. / This thesis uses a magnification technique to magnify the subtle, faint and spontaneous facial muscle movements or more precisely, micro-expressions. This magnification would help a system in classifying them and estimating the emotion behind them. This technique additionally magnifies the color changes, which could be used to extract the pulse without a physical contact with the subject. The results are presented in a GUI. / Gautam: +46(0)739528573, +91-9701534064 Tushal: +46(0)723219833, +91-9000242241 Venu: +46(0)734780266, +91-9298653191 Sai: +91-9989410111