Mohamed A. Bencherif

Fusion of Extreme Learning Machine and Graph-Based Optimization Methods for Active Classification of Remote Sensing Images

In this letter, we propose an efficient multiclass active learning (AL) method for remote sensing image classification. We fuse the capabilities of an extreme learning machine (ELM) classifier and graph-based optimization methods to boost the classification accuracy while minimizing the user interaction. First, we use the ELM to generate an initial label estimation of the unlabeled image pixels. Then, we optimize a graph-based functional energy that integrates the ELM outputs as an initial estimation of the image structure. As for the ELM, the solution to this multiclass optimization problem leads to a system of linear equations. Due to the sparse Laplacian matrix built from the lattice graph defined on the image pixels, the optimization problem is solved in a linear time. In the experiments, we report and discuss the results of the proposed AL method on two very high resolution images acquired by IKONOS-2 and GoeEye-1, as well as the well-known Pavia University hyperspectral image.

An Investigation of Multidimensional Voice Program Parameters in Three Different Databases for Voice Pathology Detection and Classification

BACKGROUND AND OBJECTIVE Automatic voice-pathology detection and classification systems may help clinicians to detect the existence of any voice pathologies and the type of pathology from which patients suffer in the early stages. The main aim of this paper is to investigate Multidimensional Voice Program (MDVP) parameters to automatically detect and classify the voice pathologies in multiple databases, and then to find out which parameters performed well in these two processes. MATERIALS AND METHODS Samples of the sustained vowel /a/ of normal and pathological voices were extracted from three different databases, which have three voice pathologies in common. The selected databases in this study represent three distinct languages: (1) the Arabic voice pathology database; (2) the Massachusetts Eye and Ear Infirmary database (English database); and (3) the Saarbruecken Voice Database (German database). A computerized speech lab program was used to extract MDVP parameters as features, and an acoustical analysis was performed. The Fisher discrimination ratio was applied to rank the parameters. A t test was performed to highlight any significant differences in the means of the normal and pathological samples. RESULTS The experimental results demonstrate a clear difference in the performance of the MDVP parameters using these databases. The highly ranked parameters also differed from one database to another. The best accuracies were obtained by using the three highest ranked MDVP parameters arranged according to the Fisher discrimination ratio: these accuracies were 99.68%, 88.21%, and 72.53% for the Saarbruecken Voice Database, the Massachusetts Eye and Ear Infirmary database, and the Arabic voice pathology database, respectively.

Detection of premature ventricular contraction arrhythmias in electrocardiogram signals with kernel methods

In this paper, we propose to investigate the capabilities of two kernel methods for the detection and classification of premature ventricular contractions (PVC) arrhythmias in Electrocardiogram (ECG signals). These kernel methods are the support vector machine and Gaussian process (GP). We propose to study these two classifiers with various feature representations of ECG signals, such as morphology, discrete wavelet transform, higher-order statistics, and S transform. The experimental results obtained on 48 records (i.e., 109,887 beats) of the MIT-BIH Arrhythmia database showed that for all feature representation adopted in this work, the GP detector trained only with 600 beats from PVC and Non-PVC classes can provide an overall accuracy and a sensitivity above 90 % on 20 records (i.e., 49,774 beats) and 28 records (i.e., 60,113 beats) seen and unseen, respectively, during the training phase.

KSU Speech Database: Text Selection, Recording and Verification

King Saud University speech database (KSU-DB) is a very rich speech database of Arabic language. Its richness is in many dimensions. It has more than three hundred speakers of both genders. The speakers are Arabs and non-Arabs belonging to twenty-nine different nationalities. The database has different types of text such as isolated words, digits, phonetically rich words and sentences, phonetically balanced sentences, paragraphs, and answers to questions. The KSU-DB was recorded in three different locations, the first is an office that represents a normal environment with low noise. The second and third locations are cafeteria and soundproof room representing noisy and quiet environments, respectively. The database has different channels of recordings, mobile, medium and high quality microphones connected to recording devices of different qualities. To track the inter-session variations of the speakers, the database was recorded in three sessions with a gap of about six weeks. Though the database main goal is for speaker recognition research, nonetheless, we made it very rich so that it can be used in many speech-processing researches. A team of native Arabs verified the database manually as well as automatically.

Building a Rich Arabic Speech Database

Availability of databases is a necessity in the speech processing field. The publically available databases in Arabic language are few. In this paper we describe a rich database for Arabic language. The database is rich in many dimensions: in text, environments, microphone type, number of recording sessions, recording system, the transmission channel, the country of origin, and the mother language. This richness makes the database an important resource for research in Arabic Language processing and very useful in many speech processing tasks, such as speaker recognition, speech recognition, and accent identification. The speakers were speaking in Modern Standard Arabic (MSA).

A technique to overcome the problem of small size database for automatic speaker recognition

Modeling a system by statistical methods needs large amount of data to train the system. In real life such data are sometimes not available or hard to collect. Modeling the system with small size database will produce a system with poor performance. In this paper we propose a method for increasing the size of the database. The method works by generating new samples from the original samples, using combinations of the following methods: speech lengthening, noise adding, and word reversal. To make a proof of concept, we used a severe test condition, in which the original database consists of one sample per speaker, for a speaker recognition system. We tested the system using original samples. The best results were 90% and 90.41% recognition rates for two subsets of the database for 25 and 50 speakers respectively.

Mobile robot nonlinear feedback control based on Elman neural network observer

This article presents a new approach to control a wheeled mobile robot without velocity measurement. The controller developed is based on kinematic model as well as dynamics model to take into account parameters of dynamics. These parameters related to dynamic equations are identified using a proposed methodology. Input–output feedback linearization is considered with a slight modification in the mathematical expressions to implement the dynamic controller and analyze the nonlinear internal behavior. The developed controllers require sensors to obtain the states needed for the closed-loop system. However, some states may not be available due to the absence of the sensors because of the cost, the weight limitation, reliability, induction of errors, failure, and so on. Particularly, for the velocity measurements, the required accuracy may not be achieved in practical applications due to the existence of significant errors induced by stochastic or cyclical noise. In this article, Elman neural network is proposed to work as an observer to estimate the velocity needed to complete the full state required for the closed-loop control and account for all the disturbances and model parameter uncertainties. Different simulations are carried out to demonstrate the feasibility of the approach in tracking different reference trajectories in comparison with other paradigms.

Automatic Speaker Recognition for Mobile Forensic Applications

Presently, lawyers, law enforcement agencies, and judges in courts use speech and other biometric features to recognize suspects. In general, speaker recognition is used for discriminating people based on their voices. The process of determining, if a suspected speaker is the source of trace, is called forensic speaker recognition. In such applications, the voice samples are most probably noisy, the recording sessions might mismatch each other, the sessions might not contain sufficient recording for recognition purposes, and the suspect voices are recorded through mobile channel. The identification of a person through his voice within a forensic quality context is challenging. In this paper, we propose a method for forensic speaker recognition for the Arabic language; the King Saud University Arabic Speech Database is used for obtaining experimental results. The advantage of this database is that each speaker’s voice is recorded in both clean and noisy environments, through a microphone and a mobile channel. This diversity facilitates its usage in forensic experimentations. Mel-Frequency Cepstral Coefficients are used for feature extraction and the Gaussian mixture model-universal background model is used for speaker modeling. Our approach has shown low equal error rates (EER), within noisy environments and with very short test samples.