Vennila Ramalingam

Classification of audio signals using SVM and RBFNN

In the age of digital information, audio data has become an important part in many modern computer applications. Audio classification has been becoming a focus in the research of audio processing and pattern recognition. Automatic audio classification is very useful to audio indexing, content-based audio retrieval and on-line audio distribution, but it is a challenge to extract the most common and salient themes from unstructured raw audio data. In this paper, we propose effective algorithms to automatically classify audio clips into one of six classes: music, news, sports, advertisement, cartoon and movie. For these categories a number of acoustic features that include linear predictive coefficients, linear predictive cepstral coefficients and mel-frequency cepstral coefficients are extracted to characterize the audio content. Support vector machines are applied to classify audio into their respective classes by learning from training data. Then the proposed method extends the application of neural network (RBFNN) for the classification of audio. RBFNN enables nonlinear transformation followed by linear transformation to achieve a higher dimension in the hidden space. The experiments on different genres of the various categories illustrate the results of classification are significant and effective.

Automated assessment of breast tissue density in digital mammograms

Mammographic density is known to be an important indicator of breast cancer risk. Classification of mammographic density based on statistical features has been investigated previously. However, in those approaches the entire breast including the pectoral muscle has been processed to extract features. In this approach the region of interest is restricted to the breast tissue alone eliminating the artifacts, background and the pectoral muscle. The mammogram images used in this study are from the Mini-MIAS digital database. Here, we describe the development of an automatic breast tissue classification methodology, which can be summarized in a number of distinct steps: (1) preprocessing, (2) feature extraction, and (3) classification. Gray level thresholding and connected component labeling is used to eliminate the artifacts and pectoral muscles from the region of interest. Statistical features are extracted from this region which signify the important texture features of breast tissue. These features are fed to the support vector machine (SVM) classifier to classify it into any of the three classes namely fatty, glandular and dense tissue.The classifier accuracy obtained is 95.44%.

Breast mass classification based on cytological patterns using RBFNN and SVM

Correct diagnosis is one of the major problems in medical field. This includes the limitation of human expertise in diagnosing the disease manually. From the literature it has been found that pattern classification techniques such as support vector machines (SVM) and radial basis function neural network (RBFNN) can help them to improve in this domain. RBFNN and SVM with their remarkable ability to derive meaning from complicated or imprecise data, can be used to extract patterns and detect trends that are too complex to be noticed by either humans or other computer techniques. This paper compares the use of polynomial kernel of SVM and RBFNN in ascertaining the diagnostic accuracy of cytological data obtained from the Wisconsin breast cancer database. The data set includes nine different attributes and two categories of tumors namely benign and malignant. Known sets of cytologically proven tumor data was used to train the models to categorize cancer patients according to their diagnosis. Performance measures such as accuracy, specificity, sensitivity, F-score and other metrics used in medical diagnosis such as Youdens index and discriminant power were evaluated to convey and compare the qualities of the classifiers. This research has demonstrated that RBFNN outperformed the polynomial kernel of SVM for correctly classifying the tumors.

Facial expression recognition - A real time approach

Face localization, feature extraction, and modeling are the major issues in automatic facial expression recognition. In this paper, a method for facial expression recognition is proposed. A face is located by extracting the head contour points using the motion information. A rectangular bounding box is fitted for the face region using those extracted contour points. Among the facial features, eyes are the most prominent features used for determining the size of a face. Hence eyes are located and the visual features of a face are extracted based on the locations of eyes. The visual features are modeled using support vector machine (SVM) for facial expression recognition. The SVM finds an optimal hyperplane to distinguish different facial expressions with an accuracy of 98.5%.

Classification of audio signals using AANN and GMM

Today, digital audio applications are part of our everyday lives. Audio classification can provide powerful tools for content management. If an audio clip automatically can be classified it can be stored in an organised database, which can improve the management of audio dramatically. In this paper, we propose effective algorithms to automatically classify audio clips into one of six classes: music, news, sports, advertisement, cartoon and movie. For these categories a number of acoustic features that include linear predictive coefficients, linear predictive cepstral coefficients and mel-frequency cepstral coefficients are extracted to characterize the audio content. The autoassociative neural network model (AANN) is used to capture the distribution of the acoustic feature vectors. The AANN model captures the distribution of the acoustic features of a class, and the backpropagation learning algorithm is used to adjust the weights of the network to minimize the mean square error for each feature vector. The proposed method also compares the performance of AANN with a Gaussian mixture model (GMM) wherein the feature vectors from each class were used to train the GMM models for those classes. During testing, the likelihood of a test sample belonging to each model is computed and the sample is assigned to the class whose model produces the highest likelihood.

Automatic classification of Tamil documents using vector space model and artificial neural network

Automatic text classification based on vector space model (VSM), artificial neural networks (ANN), K-nearest neighbor (KNN), Naives Bayes (NB) and support vector machine (SVM) have been applied on English language documents, and gained popularity among text mining and information retrieval (IR) researchers. This paper proposes the application of VSM and ANN for the classification of Tamil language documents. Tamil is morphologically rich Dravidian classical language. The development of internet led to an exponential increase in the amount of electronic documents not only in English but also other regional languages. The automatic classification of Tamil documents has not been explored in detail so far. In this paper, corpus is used to construct and test the VSM and ANN models. Methods of document representation, assigning weights that reflect the importance of each term are discussed. In a traditional word-matching based categorization system, the most popular document representation is VSM. This method needs a high dimensional space to represent the documents. The ANN classifier requires smaller number of features. The experimental results show that ANN model achieves 93.33% which is better than the performance of VSM which yields 90.33% on Tamil document classification.

Measuring advertisement effectiveness—a neural network approach

Abstract This study aims to incorporate Artificial Neural Network (ANN) for measuring the effectiveness of the TV broadcast advertisements (toothpaste) by discovering important factors that influence the advertisement effectiveness. The information about the effects of each of these factors has been studied and it is used for measuring the advertisement effectiveness. Fifty attributes are examined to derive values from thirteen factors. These thirteen factors are used as input to ANN model. The data collected from 837 respondents are used for training and testing the ANN. The backpropagation algorithm is used for adjusting the weights in the ANN. Experimental results show that the ANN model achieves 99% accuracy for measuring the advertisement effectiveness.

Multimodel decision support system for psychiatry problem

Psychological distress and disabilities are increasingly identified among general population. Psychiatrist availability in rural areas is poor and often general practitioners have to identify and treat psychiatric problems like depression and anxiety. This work proposes a method to identify the psychiatric problems among patients using multimodel decision support system. Backpropagation neural networks (BPNN), radial basis function neural network (RBFNN) and support vector machine (SVM) models are used to design the decision support system. Forty-four factors are considered for feature extraction. The features are collected from 400 patients and divided into four sets of equal size. Three sets of patient features are used to train the decision support system and one set of patient feature are used to evaluate performance of the system. Experimental results show that the proposed method achieves an accuracy of 98.75% for identifying the psychiatric problems.

Speaker diarization using autoassociative neural networks

This paper addresses a new approach to speaker diarization using autoassociative neural networks (AANN). The speaker diarization task consists of segmenting a conversation into homogeneous segments which are then clustered into speaker classes. The proposed method uses AANN models to capture the speaker specific information from mel frequency cepstral coefficients (MFCC). The distribution capturing ability of the AANN model is utilized for segmenting the conversation and grouping each segment into one of the speaker classes. The algorithm has been tested on different databases, and the results are compared with the existing algorithms. The experimental results show that the proposed approach competes with the standard speaker diarization methods reported in the literature and it is an alternative method to the existing speaker diarization methods.

Unsupervised speaker segmentation with residual phase and MFCC features

This paper proposes an unsupervised method for improving the automatic speaker segmentation performance by combining the evidence from residual phase (RP) and mel frequency cepstral coefficients (MFCC). This method demonstrates the complementary nature of speaker specific information present in the residual phase in comparison with the information present in the conventional MFCC. Moreover this method presents an unsupervised speaker segmentation algorithm based on support vector machine (SVM). The experiments show that the combination of residual phase and MFCC helps to identify more robustly the transitions among speakers.