Fakhri Karray

Survey on speech emotion recognition: Features, classification schemes, and databases

Recently, increasing attention has been directed to the study of the emotional content of speech signals, and hence, many systems have been proposed to identify the emotional content of a spoken utterance. This paper is a survey of speech emotion classification addressing three important aspects of the design of a speech emotion recognition system. The first one is the choice of suitable features for speech representation. The second issue is the design of an appropriate classification scheme and the third issue is the proper preparation of an emotional speech database for evaluating system performance. Conclusions about the performance and limitations of current speech emotion recognition systems are discussed in the last section of this survey. This section also suggests possible ways of improving speech emotion recognition systems.

Retinopathy Online Challenge: Automatic Detection of Microaneurysms in Digital Color Fundus Photographs

The detection of microaneurysms in digital color fundus photographs is a critical first step in automated screening for diabetic retinopathy (DR), a common complication of diabetes. To accomplish this detection numerous methods have been published in the past but none of these was compared with each other on the same data. In this work we present the results of the first international microaneurysm detection competition, organized in the context of the Retinopathy Online Challenge (ROC), a multiyear online competition for various aspects of DR detection. For this competition, we compare the results of five different methods, produced by five different teams of researchers on the same set of data. The evaluation was performed in a uniform manner using an algorithm presented in this work. The set of data used for the competition consisted of 50 training images with available reference standard and 50 test images where the reference standard was withheld by the organizers (M. Niemeijer, B. van Ginneken, and M. D. AbrA¿moff). The results obtained on the test data was submitted through a website after which standardized evaluation software was used to determine the performance of each of the methods. A human expert detected microaneurysms in the test set to allow comparison with the performance of the automatic methods. The overall results show that microaneurysm detection is a challenging task for both the automatic methods as well as the human expert. There is room for improvement as the best performing system does not reach the performance of the human expert. The data associated with the ROC microaneurysm detection competition will remain publicly available and the website will continue accepting submissions.

Retinal vessel extraction by matched filter with first-order derivative of Gaussian

Accurate extraction of retinal blood vessels is an important task in computer aided diagnosis of retinopathy. The matched filter (MF) is a simple yet effective method for vessel extraction. However, a MF will respond not only to vessels but also to non-vessel edges. This will lead to frequent false vessel detection. In this paper we propose a novel extension of the MF approach, namely the MF-FDOG, to detect retinal blood vessels. The proposed MF-FDOG is composed of the original MF, which is a zero-mean Gaussian function, and the first-order derivative of Gaussian (FDOG). The vessels are detected by thresholding the retinal images response to the MF, while the threshold is adjusted by the images response to the FDOG. The proposed MF-FDOG method is very simple; however, it reduces significantly the false detections produced by the original MF and detects many fine vessels that are missed by the MF. It achieves competitive vessel detection results as compared with those state-of-the-art schemes but with much lower complexity. In addition, it performs well at extracting vessels from pathological retinal images.

Detection of microaneurysms using multi-scale correlation coefficients

This paper presents a new approach to the computer aided diagnosis (CAD) of diabetic retinopathy (DR)-a common and severe complication of long-term diabetes which damages the retina and cause blindness. Since microaneurysms are regarded as the first signs of DR, there has been extensive research on effective detection and localization of these abnormalities in retinal images. In contrast to existing algorithms, a new approach based on multi-scale correlation filtering (MSCF) and dynamic thresholding is developed. This consists of two levels, microaneurysm candidate detection (coarse level) and true microaneurysm classification (fine level). The approach was evaluated based on two public datasets-ROC (retinopathy on-line challenge, http://roc.healthcare.uiowa.edu) and DIARETDB1 (standard diabetic retinopathy database, http://www.it.lut.fi/project/imageret/diaretdb1). We conclude our method to be effective and efficient.

An Efficient Concept-Based Mining Model for Enhancing Text Clustering

Most of the common techniques in text mining are based on the statistical analysis of a term, either word or phrase. Statistical analysis of a term frequency captures the importance of the term within a document only. However, two terms can have the same frequency in their documents, but one term contributes more to the meaning of its sentences than the other term. Thus, the underlying text mining model should indicate terms that capture the semantics of text. In this case, the mining model can capture terms that present the concepts of the sentence, which leads to discovery of the topic of the document. A new concept-based mining model that analyzes terms on the sentence, document, and corpus levels is introduced. The concept-based mining model can effectively discriminate between nonimportant terms with respect to sentence semantics and terms which hold the concepts that represent the sentence meaning. The proposed mining model consists of sentence-based concept analysis, document-based concept analysis, corpus-based concept-analysis, and concept-based similarity measure. The term which contributes to the sentence semantics is analyzed on the sentence, document, and corpus levels rather than the traditional analysis of the document only. The proposed model can efficiently find significant matching concepts between documents, according to the semantics of their sentences. The similarity between documents is calculated based on a new concept-based similarity measure. The proposed similarity measure takes full advantage of using the concept analysis measures on the sentence, document, and corpus levels in calculating the similarity between documents. Large sets of experiments using the proposed concept-based mining model on different data sets in text clustering are conducted. The experiments demonstrate extensive comparison between the concept-based analysis and the traditional analysis. Experimental results demonstrate the substantial enhancement of the clustering quality using the sentence-based, document-based, corpus-based, and combined approach concept analysis.

Enhancing Search Engine Quality Using Concept-based Text Retrieval

Most of the common techniques in text retrieval are based on the statistical analysis of a term either as a word or a phrase. Statistical analysis of a term frequency captures the importance of the term within a document only. Thus, to achieve a more accurate analysis, the underlying representation should indicate terms that capture the semantics of text. In this case, the representation can capture terms that present the concepts of the sentence, which leads to discover the topic of the document. A new concept-based representation, called Conceptual Ontological Graph (COG), where a concept can be either a word or a phrase and totally dependent on the sentence semantics, is introduced. The aim of the proposed representation is to extract the most important terms in a sentence and a document with respect to the meaning of the text. The COG representation analyzes each term at both the sentence and the document levels. This is different from the classical approach of analyzing terms at the document level. First, the proposed representation denotes the terms which contribute to the sentence semantics. Then, each term is chosen based on its position within the COG representation. Lastly, the selected terms are associated to their documents as features for the purpose of indexing before text retrieval. The COG representation can effectively discriminate between non-important terms with respect to sentence semantics and terms which hold the key concepts that represent the sentence meaning. Large sets of experiments using the proposed COG representation on different datasets in text retrieval are conducted. Experimental results demonstrate the substantial enhancement of the text retrieval quality using the COG representation over the traditional techniques. The evaluation of results relies on two quality measures, the bpref and P(10). Both the quality measures improved when the newly developed COG representation is used to enhance the quality of the text retrieval results.In this paper, we investigate the emotion classification of web blog corpora using support vector machine (SVM) and conditional random field (CRF) machine learning techniques. The emotion classifiers are trained at the sentence level and applied to the document level. Our methods also determine an emotion category by taking the context of a sentence into account. Experiments show that CRF classifiers outperform SVM classifiers. When applying emotion classification to a blog at the document level, the emotion of the last sentence in a document plays an important role in determining the overall emotion.

Stiffening control of a class of nonlinear affine systems

A control procedure is proposed for dealing with the active stiffening motion of a class of flexible structures characterized by nonlinear affine dynamics. Based on recent developments in the area of differential geometry, the procedure allows for determining the critical area for placing a sensor on a given flexible structure beyond which a centralized controller located on the rigid part becomes ineffective. This is used subsequently for locating instrumentation devices and hardware components on the elastic parts of the system. Numerical simulations are carried out to assess the validity of the theoretical framework. Several meaningful results are obtained, and propositions for further findings are outlined.

Inferring operating rules for reservoir operations using fuzzy regression and ANFIS

The methods of ordinary least-squares regression (OLSR), fuzzy regression (FR), and adaptive network-based fuzzy inference system (ANFIS) are compared in inferring operating rules for a reservoir operations optimization problem. Dynamic programming (DP) is used as an example optimization tool to provide the input-output data set to be used by OLSR, FR, and ANFIS models. The coefficients of an FR model are found by solving a linear programming (LP) problem. The objective function of the LP is to minimize the total fuzziness of the FR model, which is related to the width of fuzzy coefficients in the regression model. Before applying FR to the reservoir operations problem, two FR formulations and interval regression (IR) are first examined in a simple tutorial example. ANFIS is also used to derive the reservoir operating rules as fuzzy IF-THEN rules. The OLSR, FR, and ANFIS based rules are then simulated and compared based on their performance in simulation. The methods are applied to a long-term planning problem as well as to a medium-term implicit stochastic optimization model. The results indicate that FR is useful to derive operating rules for a long-term planning model, where imperfect and partial information is available. ANFIS is beneficial in medium-term implicit stochastic optimization as it is able to extract important features of the system from the generated input-output set and represent those features as general operating rules.

Speech Emotion Recognition using Gaussian Mixture Vector Autoregressive Models

It is believed that modeling temporal structure of the speech data may be useful for the problem of speech emotion recognition (T. Nwe et al., 2003). In this paper, Gaussian mixture vector autoregressive model is proposed as a statistical classifier for this task. The main motivation behind using such a model is its ability to model the dependency among extracted speech feature vectors as well as the multi-modality in their distribution. When applied to the Berlin emotional speech database, the proposed technique provides a classification accuracy of 76% versus 71% for the hidden Markov model, 67% for the k-nearest neighbors, 55% for feed-forward neural networks. The model gives also better discrimination between high-arousal, low arousal, and neutral emotions than the HMM.

Multi-objective Feature Selection with NSGA II

This paper deals with the multi-objective definition of the feature selection problem for different pattern recognition domains. We use NSGA II the latest multi-objective algorithm developed for resolving problems of multi-objective aspects with more accuracy and a high convergence speed. We define the feature selection as a problem including two competing objectives and we try to find a set of optimal solutions so called Pareto-optimal solutions instead of a single optimal solution. The two competing objectives are the minimization of both the number of used features and the classification error using 1-NN classifier. We apply our method to five databases selected from the UCI repository and we report the results on these databases. We present the convergence of the NSGA II on different problems and discuss the behavior of NSGA II on these different contexts.