Informatics Publishing Limited

A. Indumathi ¹, E. Chandra ²

Affiliations
1 Department of Master of Computer Applications, Dr. SNS Rajalakshmi College of Arts and Science, IN
2 Department of Computer Science, Bharathiar University, IN

Abstract

Noise estimation and suppression is very important for improving the quality of speech signal. Noises exist in almost all places. In reality, more than one noise degrades the speech signal. It is hard to find and supress various types of noise that affect the speech quality. This paper proposed a method for noise estimation of mixed non-stationary noisy speech signal. This method uses Spectral properties of the noisy speech signal to detect the frequency regions of noise signal. Highest frequency of speech signal is calculated and it is considered as the threshold value for separating noise signal and clean speech signal. Using Spectral subtraction, Standard deviation of noise spectrum is subtracted with noisy spectrum to acquire enhanced speech signal. Performance of the method is evaluated using SNR and Spectrogram. The main focus of this paper is to propose an independent method which estimates the noise of any type and nature.

Keywords

Noise Estimation, Spectral Subtraction, Standard Deviation, SNR, Spectrogram.

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E. Chandra ¹, D. Francis Xavier Christopher ², S. D. Vijaykumar ²

Affiliations
1 Department of Computer Applications, D.J. Academy for Managerial Excellence, Coimbatore, IN
2 School of Computer Studies, RVS College of Arts and Science, Coimbatore, IN

Abstract

It focuses specifically on measuring quality and methods to enhance the quality of educational institutions with specific attributes. The CMM models like SW-CMM, People CMM are analyzed and the concern Key Process area (KPA's) attributes relevant to education industry are implemented with an automation process. Here the quality analysis is done with two popular improvement models which share a common concern with quality&process management - the ISO 9001 Standard&the SW- CMM developed by SEI. The authors then explore the scope of augmenting the SW-CMM model by incorporating additional KPA to meet the special needs of Education Industry.

Keywords

CMM, ISO Key Process Area (KPA’s), Process Framework, Maturity Model, Tool Specification.

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E. Chandra ¹, P. Edith Linda ²

Affiliations
1 Department of Computer Applications in D. J. Academy for Managerial Excellence, Coimbatore, IN
2 Area of Software Metrics under Karunya University, IN

Abstract

The design and development of any project has got a well defined project development cycle. But once the project or the product has been developed it is subject to change due to a lot a policy changes on the part of the organization or the government. These changes are implemented on the code but most of the time these changes are not reflected on the design document. This leads to inconsistencies in terms of design and code thereby causing depreciation in terms of quality. In this work we propose to use the object oriented metrics to asses the quality of the software maintained by third party. The proposed work developed will be used to determine the maximum breakthrough period of the software.

Keywords

OO Metrics, Weighted Method Per Class, Depth of Inheritance Tree.

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E. Chandra ¹, K. Kanagalakshmi ²

Affiliations
1 Department of Computer Science, DJ Academy for Managerial Excellence, Coimbatore, Tamilnadu, IN
2 Department of Computer Science, DJ Academy for Managerial Excellence, Coimbatore, Tamilnadu, IN

Abstract

Filtering and Image Enhancements are the primary need of the automatic identification and authentication system. This paper aims to review and evaluate the frequency domain enhancement techniques: Ideal Low Pass filtering (ILPF), Butterworth Low Pass Filtering (BLPF), Band Pass Filtering (BPF), and Log-Gabor Filtering. Experimental results show the performance measures based on Peak-Signal to Noise Ratio (PSNR) and Mean Square Error (MSE) and also Standard Deviation between original and enhanced image.

Keywords

Band-Pass Filter, Butterworth Filter, Domain, Log-Gabor, Low-Pass.

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E. Chandra ¹, N. Abirami ²

Affiliations
1 Department of Computer Applications, D J Academy for Managerial Excellence, Coimbatore, Tamilnadu, IN
2 Department of Computer Science, Karpagam University, Coimbatore, Tamilnadu, IN

Abstract

In this era of computing data’s are represented as images either as compressed documents or as video images itself. Since data is transmitted as images, they have to be processed for retrieval. Image retrieval is since a challenging issue in the area of computer research as systems that are currently evolving around for this purpose are not able to give an accurate level of retrieval of images as per expectations. Hence there is a high demand for such a system that can evaluate the images and fetches images of much accuracy as possible and at the earliest possible time. The aim of the paper is to improve efficiency of image retrieval, a new image retrieval scheme that applies sub-image processing with low level features of image such as color and shape embedded with segmentation and relevance feedback. It also applies local feature descriptor attributes that are computed on regions of the image. So, a combination of hybrid features and techniques are used to form a retrieval system.

Keywords

Content Based Image Retrieval (CBIR), Content Based Sub-Image Retrieval (CBSIR), Image Features, Color Spaces, Segmentation.

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E. Chandra ¹, V. P. Anuradha ²

Affiliations
1 Department of Computer Science, DJ Academy for Managerial Excellence, Coimbatore, IN
2 Karpagam University, Coimbatore, IN

Abstract

Query processing is an essential task to obtain meaningful information in a data mining application. It needs to be optimized for its effective implementation in any application. A join operation of relational data base management system is one such technique that can optimize the query process efficiently. In a similar manner the join operation in a spatial data base management system can be utilized to optimize the query process. Join operation itself is accelerated by the implementation of join indexes further optimizing the query process. Efficient implementation of join indices is possible with such multidimensional indexing structures as R-trees, Grid files, Bi-partite graphs, Neighbourhood graphs, etc. An effective join processing algorithm in collusion with the join index enhances the query process further. A cost model with CPU cost, I/O cost, number of page and node accesses with a constraint of fixed buffer size has to be evaluated to check the feasibility of the join operation.

Keywords

Spatial Databases, Join Indices, Join Operation, Multidimensional Indexing Structures.

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E. Chandra ¹, P. Ajitha ¹

Affiliations
1 Department of Computer Science, D J Academy for Managerial Excellence, Coimbatore-32, IN

Abstract

Data mediated knowledge discovery is essential for any end users for value added decision making. Discerning vital, accurate and precise knowledge in the classification, various feature subsets are necessary. Apart from feature selection, processing and representation of data is also indispensable for analysis and implementation of any knowledge. Principal Component Analysis is the used for data pre-processing and representation of data. Eigen vectors, co variance matrix are estimated for distributed environment where local and global set are computed and evaluated. It reduces the dimensionality of data. RELIEF, CMIM and other feature selection methods are discussed here in this paper. On selecting the features may increase the classification accuracy and enhance classification and prediction.

Keywords

Feature Selection, Models, Distributed Data Mining, PCA, Classification, CMIM, mRMR, RELIEF.

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E. Chandra ¹, K. Nandhini ²

Affiliations
1 Department of Computer Applications, D. J. Academy for Managerial Excellence, Coimbatore-32, Tamilnadu, IN
2 Computer Science Department, Dr. N. G. P. Arts and Science College, Coimbatore-48, Tamilnadu, IN

Abstract

The ability to predicting the performance of a student is very essential task of all educational institutions. This will not be decided by using only the academic excellence of a student. The behaviors such as aptitude, attitude, communications, technological, interpersonally, problem solving ability etc., should be taken into care to predict the real excellence of a student. This form a heterogeneous dataset covering cross section of categorical, integer type data types etc. This has given rise to a high dimensional dataset which will hamper classification process. Since this is the task of prediction and mining the classification algorithms of data mining is used. The decision tree algorithms of classification are one of the fine grained methods to bring the more accuracy of prediction. The first phase of the work is collecting the wide cross section of atabase of values for attributes which are quite cross functional. The second phase plays vital role for effective classification by narrowing down by selection of predictive attributes. This phase is done by Feature Extraction techniques to reduce the high dimensional dataset in to a low dimensional dataset. The third phase applying the algorithms uses the Naive Bayes and tree induction of decision tree methods for actual classification of the data. The scalability of these methods has improved by perception based learning. Also, there is a school of thought that one can take up the classification and data mining without incorporating any Dimensionality reduction techniques like Feature Extraction. This work compare results obtained by the both process and study the performance of the Prediction accuracy. It is not that only the student domain can be used for excellence prediction. It can be applied for any kind of domain.

Keywords

Data Mining, Decision Tree, Feature Extraction, Performance Prediction.

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S. Karpagavalli ¹, E. Chandra ²

Affiliations
1 Department of Computer Science, PSGR Krishnammal College for Women, Coimbatore - 641004, Tamil Nadu, IN
2 Department of Computer Science, Bharathiar University, Coimbatore - 641046, Tamil Nadu, IN

Abstract

Most of the speech recognition systems are designed based on the sub-word unit phoneme which is the basic sound unit of a language. In the proposed work, a novel hierarchical approach based phoneme classification task has been carried out to reduce time complexity and search space. Hierarchical classification of set of Tamil phonemes has been done in three levels. Phoneme boundaries of the given speech utterance are identified using Spectral Transition Measure (STM) and phonemes are separated. Mel-Frequency Cepstral Coefficients (MFCC) are extracted for each phoneme represented by 9 frames including the contextual frames of corresponding phoneme. In each hierarchical level, different number of models is built using Support Vector Machine (SVM) for classifying each phoneme group/phoneme. It is observed from the results that in hierarchical approach phoneme group recognition rate at level 1 and 2 has greatly improved compared to flat classification model. Complexity of search space is significantly reduced at level 2 and level 3 contrasts to flat phoneme classification model. Hierarchical phoneme classifier can be very well employed in phoneme recognition task which is useful in applications such as spoken term detection, out-ofvocabulary detection, named entity recognition, spoken document retrieval.

Keywords

Mel-Frequency Cepstral Coefficients, Spectral Transition Measure

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K. Manikandan ¹, E. Chandra ²

Affiliations
1 Department of Computer Science, PSG College of Arts and Science, Coimbatore – 641015, Tamil Nadu, IN
2 Department of Computer Science, Bharathiar University, Coimbatore – 641046, IN

Abstract

Objectives: The proposed speaker identification using a novel prosody with fuzzy based hierarchical decision tree approach and is used to modifying the limitations of existing traditional methods. It improves the performance of speaker identification in given population under noisy environments. Methods/Statistics: The key idea of this approach is to achieve an enhanced efficiency speaker cluster group using prosody features with fuzzy clustering at each level to construct the hierarchical decision tree. At each level, a speaker at belong to same groups are constructed. The proposed method has novelty of prosody as pitch and loudness with fuzzy clustering are used. Findings: An experimental result shows that the proposed model using prosody features outperforms the efficiency of speaker accuracy rate of 93.75 when compare to vocal source accuracy rate of 81.25 under noisy environments. Applications: Gender and age identification, banking and smart voice based technology operation.

Keywords

Fuzzy Clustering, Large Population Speaker Identification, Prosody Feature Extraction, Prosody with Fuzzy based Hierarchical Decision Tree.

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N. Shanmugapriya ¹, E. Chandra ²

Affiliations
1 Department of Computer Applications, Dr. SNS Rajalakshmi College of Arts and Science, IN
2 Department of Computer Science, Bharathiar University, IN

Abstract

Hearing aid users are exposed to diversified vocal scenarios. The necessity for sound classification algorithms becomes a vital factor to yield good listening experience. In this work, an approach is proposed to improve the speech quality in the hearing aids based on Independent Component Analysis (ICA) algorithm with modified speech signal classification methods. The proposed algorithm has better results on speech intelligibility than other existing algorithm and this result has been proved by the intelligibility experiments. The ICA algorithm and modified Bayesian with Adaptive Neural Fuzzy Interference System (ANFIS) is to effectiveness of the strategies of speech quality, thus this classification increases noise resistance of the new speech processing algorithm that proposed in this present work. This proposed work indicates that the new Modified classifier can be feasible in hearing aid applications.

Keywords

Independent Component Analysis (ICA), Speech Intelligibility, Bayesian Modified with Adaptive Neural Fuzzy Interference System (ANFIS).

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A. Akila ¹, E. Chandra ²

Affiliations
1 D.J. Academy for Managerial Excellence, IN
2 Department of Computer Science, Bharathiar University, IN

Abstract

Speech recognition system requires segmentation of speech waveform into fundamental acoustic units. Segmentation is a process of decomposing the speech signal into smaller units. Speech segmentation could be done using wavelet, fuzzy methods, Artificial Neural Networks and Hidden Markov Model. Speech segmentation is a process of breaking continuous stream of sound into some basic units like words, phonemes or syllable that could be recognized. Segmentation could be used to distinguish different types of audio signals from large amount of audio data, often referred as audio classification. The speech segmentation can be divided into two categories based on whether the algorithm uses previous knowledge of data to process the speech. The categories are blind segmentation and aided segmentation.

The major issues with the connected speech recognition algorithms were the vocabulary size will be larger with variation in the combination of words in the connected speech and the complexity of the algorithm is more to find the best match for the given test pattern. To overcome these issues, the connected speech has to be segmented into words using the attributes of speech. A methodology using the temporal feature Short Term Energy was proposed and compared with an existing algorithm called Dynamic Thresholding segmentation algorithm which uses spectrogram image of the connected speech for segmentation.

Keywords

Short Term Energy, Missed Detection Percentage, Deviation Percentage, Dynamic Thresholding Segmentation, Temporal Feature Based Segmentation.

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S. Karpagavalli ¹, E. Chandra ²

Affiliations
1 Department of Computer Science, PSGR Krishnammal College for Women, IN
2 Department of Computer Science, Bharathiar University, IN

Abstract

Tamil Language is one of the ancient Dravidian languages spoken in south India. Most of the Indian languages are syllabic in nature and syllables are in the form of Consonant-Vowel (CV) units. In Tamil language, CV pattern occurs in the beginning, middle and end of a word. In this work, CV Units formed with Stop Consonant - Short Vowel (SCSV) were considered for classification task. The work carried out in three stages, Vowel Onset Point (VOP) detection, CV segmentation and classification. VOP is an event at which the consonant part ends and vowel part begins. VOPs are identified using linear prediction residuals which provide significant characteristics of the excitation source. To segment the CV units, fixed length spectral frames before and after VOPs are considered. In this work, production based features, Linear Predictive Cepstral Coefficients (LPCC) and perception based features, Perceptual Linear Predictive Cepstral Coefficients (PLP) and Mel Frequency Cepstral Coefficients (MFCC) are extracted which are used to build the SCSV classifier using multilayer perceptron and support vector machine. A speech corpus of 200 Tamil words uttered by 15 native speakers was used, which covers all SCSV units formed with Tamil stop consonants (/k/, /ch/, /d/, /t/, /p/) and short vowels (/a/, /i/, /u/, /e/, /o/). The classifiers are trained and tested for its performance using predictive accuracy measure. The results indicate that perception based features, MFCC and PLP provides better results than production based features, LPCC and the model built using support vector machine outperforms.

Keywords

Syllables, Consonant-Vowel Unit, Vowel Onset Point, Multilayer Perceptron, Support Vector Machine.

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E. Chandra ¹, K. Kanagalakshmi ²

Affiliations
1 Department of Computer Science, DJ Academy for Managerial Excellence, Coimbatore (DT), Tamilnadu, IN
2 Department of Computer Science, DJ Academy for Managerial Excellence, Coimbatore, Tamilnadu, IN

Abstract

Fingerprint recognition is a promising factor for the Biometric Identification and authentication process. The quality of the fingerprint is obtained by the noise free image. To get a noise-free fingerprint image, the pre-processing techniques are applied on image. In this paper, we described the finger print classifications, characteristics and pre-processing techniques. We applied the histogram on 256 gray scale finger print image with the default threshold value;then the histogram-equalized image is obtained. Next, histogram-equalized image is given under the binarization process. Finally the binarized fingerprint image is filtered with the implementation of the Median filtering technique in order to produce the noise free image. The comparison of the median filtered image with the original noisy image shows the depth of the noise spread in the original image. The experimental result shows the noise rate which was eliminated in the input fingerprint image and quality of the filtered image using the Statistical-Correlation tool.

Keywords

Authentication, Binarization, Histogram, Identification, Median Filter.

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