Eugen Lupu

On the speaker verification using the TESPAR coding method

This work describes study on the speaker verification rate, using the TESPAR (Time Encoding Signal Processing and Recognition) coding method, when the speech signal is sampled at different rates. The effect of filtering on the speech signal was studied, as well. The TESPAR method is a processing and recognition method in the time domain, proposed by R.A. King. The key problem is to define the TESPAR alphabet used for the TESPAR coding process. In this paper is proposed an approach to generate this alphabet using the Kohonen Neural Networks in a vector quantization process. For the recognition process parallel Multi Layer Perceptron (MLP) neural network were used. As inputs for training/test vectors of the MLP-NN, the TESPAR-S matrices were employed.

On-line signature recognition approach based on wavelets and Support Vector Machines

In this work, a new on-line signature verification system is proposed. Firstly, the pen-position parameters of the online signature are decomposed into multiscale signals by using the wavelet transform technique. A TESPAR DZ based method is employed to code the approximation and details coefficients. Thus, for each analyzed time function, a fixed dimension feature vector is obtained. Experimental results were reported using the SVC2004 database. The models were trained and tested with the Support Vector Machine classifier. A feature level fusion strategy was adapted.

A new set of features for a bimodal system based on on-line signature and speech

In this work, a new set of features is presented for a biometric system based on speech and on-line signature. The feature vector is nonhomogeneous and it comprises using TESPAR DZ coefficients, wavelet energy coefficients and also some additional features resulted from the time domain analysis in the case of speech. A feature selection procedure is then applied to reduce the feature vector dimension. A modified symbols alphabet for the TESPAR DZ method is presented. Experimental results were reported using the SVC2004 database for signature and our own bimodal database BimDB10 (for on-line signature and speech). A feature level fusion strategy was adapted in order to achieve our goals. The results show that the fusion of biometric features brings improvement to the system performance.

DNA repeats detection using BW spectrograms

The presence of repeated sequences is a fundamental feature of genomes and detection of repeats is important in biology and medicine as it can be used for phylogenic studies and disease diagnosis. A major difficulty in identification of repeats arises from the fact that the repeat units can be either exact or imperfect, in tandem or dispersed, and of unspecified length. This paper presents results obtained by combining BW spectrograms with a novel numerical representation to isolate position and length of tandem repeats (TRs) in DNA sequences.

Bimodal approach in emotion recognition using speech and facial expressions

This paper aims to present a multimodal approach in emotion recognition which integrates information from both facial expressions and speech signal. Using two acted databases on different subjects, we were able to emphasize six emotions: sadness, anger, happiness, disgust, fear and neutral state. The models in the system were designed and tested by using a Support Vector Machine classifier. Firstly, the analysis of the strengths and the limitations of the systems based only on facial expressions or speech signal was performed. Data was then fused at the feature level. The results show that in this case the performance and the robustness of the emotion recognition system have been improved.

A new approach to iris recognition

This paper focuses on the development and implementation of a biometric system based on iris. Our approach is different from the most common solution presented in the references, as it proposes a mixture between the wavelet and the TESPAR DZ approaches applied to images. The results provided by our experiments are close to those presented in the literature.

A generic platform to study the basic aspects of DVB-IPTV conversion process

Paper describes briefly the basic concepts and a practical implementation for an original generic platform able to acquire, create and distribute IPTV content, using as primary sources DVB or locally stored video content. A clearly unusual, original feature, is the fact that the system includes both components of the bridge, RF/baseband DVB processing and IPTV streaming elements in an integrated architecture. The architecture is conceived to implement multiple test scenarios. This is particularly important in the educational environment, to clarify this mélange of technologies ranging from video acquisition or RF transmission to networking and embedded systems. Audio-video content is gathered by different traditional technologies: DVB-S, CATV Headend, video streaming servers. This experimental network is intended to be used for throughput, QoS or audio-video quality measurements. The system is used to test even possible hardware/software implementations of commercial systems. The versatility of this system is proven by implementation of few basic test scenarios. Most relevant features of the system and tests implemented in this configuration are described in the final paragraphs of the paper.

Combustion sound classification employing Gaussian Mixture Models

This paper presents a method suitable for the detection of various states of combustion in progress by means of sound analogy analysis. Visual inspection, electro-chemical transducers or analyzing the sound produced during the burning process consist of means by which the quality of the burning process can be assessed. The results may be used when taking decisions with the goal of optimally controlling the combustion process. Classification was performed by using the GMM (Gaussian Mixture Models), the parameters extracted from the recorded sound being the phase parameters and the MFCC (Mel-frequency cepstral coefficients) coefficients. The results prove to be promising and encourage future research in the acoustic relevance in burning quality detection.

Singing voice features by time-frequency representations

The goal of this paper is to present an overview of problems related to features of musical signals based on time-frequency distributions. We recall some aspects concerning extraction of the information from time-frequency representations as Friedman distribution and by using orthogonality and singular value decomposition. For robust identification system (singing voice) a more complete characterization of the analyzed signal is needed. Experimental results are also provided.

Patch based descriptors for iris recognition

In recent years, local texture analysis methods have gained increasing attention in many areas of image processing and computer vision. The current paper deals with iris features extraction, based on dense descriptors. A dense descriptor captures the local details, pixel by pixel over the complete image. Three different techniques were employed: Local Binary Pattern, Local Phase Quantization and Differential Excitation in order to provide both spatial and frequency information. To evaluate the proposed system, experiments were performed on the UPOL database, by using a linear SVM classification scheme. The results show that the iris micro-texture patterns such as crypts, furrows or pigment spots can be well characterized by patched based descriptors.