Mohamad Nizam Ayub

Blind source mobile device identification based on recorded call

Mel-frequency cepstrum coefficients (MFCCs) extracted from speech recordings has been proven to be the most effective feature set to capture the frequency spectra produced by a recording device. This paper claims that audio evidence such as a recorded call contains intrinsic artifacts at both transmitting and receiving ends. These artifacts allow recognition of the source mobile device on the other end through recording the call. However, MFCC features are contextualized by the speech contents, speakers characteristics and environments. Thus, a device-based technique needs to consider the identification of source transmission devices and improve the robustness of MFCCs. This paper aims to investigate the use of entropy of Mel-cepstrum coefficients to extract intrinsic mobile device features from near-silent segments, where it remains robust to the characteristics of different speakers. The proposed features are compared with five different combinations of statistical moments of MFCCs, including the mean, standard deviation, variance, skewness, and kurtosis of MFCCs. All feature sets are analyzed by using five supervised learning techniques, namely, support vector machine, naive Bayesian, neural network, linear logistic regression, and rotation forest classifier, as well as two unsupervised learning techniques known as probabilistic-based and nearest-neighbor-based algorithms. The experimental results show that the best performance was achieved with entropy-MFCC features that use the naive Bayesian classifier, which resulted in an average accuracy of 99.99% among 21 mobile devices. The method used the combination of entropy and MFCCs from near-silent frames.Entropy-MFCC features exhibit high performance against statistical moments of MFCCs.The traces of the source transmitting devices were identified from recorded call.The method can be implemented through other service providers, i.e. cellular, PSTN.

River segmentation using satellite image contextual information and Bayesian classifier

Satellite-based remote sensing imaging can provide continuous snapshots of the Earth’s surface over long periods. River extraction from remote sensing images is useful for the comprehensive study of dynamic changes of rivers over large areas. This paper presents a new method of extracting rivers by using training samples based on the mathematical morphology, Bayesian classifier and a dynamic alteration filter. The use of a training map from erosion morphology helps to extract the non-predictive river’s curves in the image. The algorithm has two phases: creating the profile to separate river area via evaluated morphological erosion and dilation, namely, a training map; and improving the river’s image segmentation using the Bayesian rule algorithm in which two consecutive filters swipe false positive (non-water area) along the image. The proposed algorithm was tested on the Kuala Terengganu district, Malaysia, an area that includes a river, a bridge, dam and a fair amount of vegetation. The results were compared with two standard methods based on visual perception and on peak signal-to-noise ratio, respectively. The novelty of this approach is the definition of the contextual information filtering technique, which provides an accurate extraction of river segmentation from satellite images.