@article{ author = {}, title = {A New Authentication System Based on Wavelet and Contourlet Transforms for Low Quality Palmprint Images}, abstract ={This paper proposed an authentication system based on low quality palmprint. For implementation of this system, first the features is extracted by using Contourlet and wavelet transforms. In the second phase, some of the features are selected by using Across Group Variance (AGV) filter. In the last phase by using a classification method, the authentication is completed. For classification we evaluated three different methods, Support Vector Machine (SVM), Revised Nearest Neighbor (RNN), and Boosted Direct Linear Discriminant Analysis (BDLD). The experiment is performed on the famous PolyU Palmprint database. The results shows that by combination of the proposed system and BDLD classifier has better performance in comparison to other methods and the same database.}, Keywords = {Palmprint, Authentication, Biometric, Contourlet Transform, Wavelet Transform, AGV}, volume = {7}, Number = {2}, pages = {3-12}, publisher = {Research Center on Developing Advanced Technologies}, url = {http://jsdp.rcisp.ac.ir/article-1-718-en.html}, eprint = {http://jsdp.rcisp.ac.ir/article-1-718-en.pdf}, journal = {Signal and Data Processing}, issn = {2538-4201}, eissn = {2538-421X}, year = {2011} } @article{ author = {}, title = {as}, abstract ={as}, Keywords = {as}, volume = {7}, Number = {2}, pages = {13-22}, publisher = {Research Center on Developing Advanced Technologies}, url = {http://jsdp.rcisp.ac.ir/article-1-721-en.html}, eprint = {http://jsdp.rcisp.ac.ir/article-1-721-en.pdf}, journal = {Signal and Data Processing}, issn = {2538-4201}, eissn = {2538-421X}, year = {2011} } @article{ author = {}, title = {Reducing the Periodic Noise Effects in Digital Image by an Adaptive Median Filter in the Frequency Domain}, abstract ={Periodic noises are repetitive patterns on digital images and decreased the visual quality of images. The various methods for reducing the effects of the periodic noise in digital images are firstly investigated. Then an intelligent median filter in the frequency domain with an acceptable computational cost is proposed. In the proposed method, the regions of noise frequencies are determined by analyzing intelligently the spectral of noisy image. Then only for the destroyed frequencies by periodic noise, a median filter with proper size in the frequency domain is applied and the spectrum of the image with reduced periodic noise is computed. The compared methods including the proposed method, the mean and the median filtering techniques, all in frequency domain are implemented not only under MATLAB environment, but also by C programming under OpenCV library. The results in different conditions show that the proposed filter shows higher performances, visually and statistically, and also in needs very lower computational cost.}, Keywords = {Periodic Noise, Filtering in Frequency Domain, Adaptive Median Filter}, volume = {7}, Number = {2}, pages = {23-36}, publisher = {Research Center on Developing Advanced Technologies}, url = {http://jsdp.rcisp.ac.ir/article-1-717-en.html}, eprint = {http://jsdp.rcisp.ac.ir/article-1-717-en.pdf}, journal = {Signal and Data Processing}, issn = {2538-4201}, eissn = {2538-421X}, year = {2011} } @article{ author = {}, title = {Improvement of Telephone Keyword Spotting Performance Using Linear Programming-Based Score Normalization}, abstract ={Conventional word spotting systems determine hypothesized keywords and their confidence score using a speech recognizer. Acceptance or rejection of these keywords is intended based on comparison of their scores with a specific threshold. It has been proved that confidence score prepared by recognizer is highly dependent on sub-word structure of each keyword. So comparing assigned scores to keywords without considering their sub-word units could causes degradation in overall performance. In this paper a novel method for confidence score normalization is proposed which is based on sub-word units of each keyword and linear programming algorithm. In proposed method, a keyword-dependent correction term is added to the score of the keyword to maximize separation of confidence score histograms of true and false occurrences. Our results show a 2% improvement in FOM compared to baseline system. Also, choosing an appropriate feature vector has been discussed in this paper.  }, Keywords = {Keyword spotting, Hidden Markov Model, Confidence score, Linear programming, Score normalization.}, volume = {7}, Number = {2}, pages = {37-48}, publisher = {Research Center on Developing Advanced Technologies}, url = {http://jsdp.rcisp.ac.ir/article-1-716-en.html}, eprint = {http://jsdp.rcisp.ac.ir/article-1-716-en.pdf}, journal = {Signal and Data Processing}, issn = {2538-4201}, eissn = {2538-421X}, year = {2011} } @article{ author = {Ahaki, Habib and Nasersharif, Babak}, title = {Designing a Currency Recognition System Based on Neural Networks Using Texture and Color of Images}, abstract ={Since money exchange is important in our daily life, many types of equipments such as Vending Machines, Currency Sorters, Automatic Teller Machines (ATM) and Currency Recognition systems for blind people have been made. More advanced devices with more capabilities are being made each day. As a result, efficient, fast and reliable currency recognition methods are required. Most currency recognition methods only use one attribute of currency images, such as major color, Ultra Violet spectrum or texture that are extracted from currency images. In this paper, we introduce a method for currency recognition that combines texture and color data together and applies them to a neural network. The best result from the other existing methods is at most 85 percent but our method shows 10 percent improvement compared to existing solutions.}, Keywords = {Currency Recognition, Image Processing, Neural Network, Digital Filter}, volume = {7}, Number = {2}, pages = {59-68}, publisher = {Research Center on Developing Advanced Technologies}, url = {http://jsdp.rcisp.ac.ir/article-1-720-en.html}, eprint = {http://jsdp.rcisp.ac.ir/article-1-720-en.pdf}, journal = {Signal and Data Processing}, issn = {2538-4201}, eissn = {2538-421X}, year = {2011} } @article{ author = {}, title = {Design and Evaluation of a Persian TTS system using prosodically-sensitive concatenative units}, abstract ={This paper describes the design and evaluation of prosodically-sensitive concatenative units for a Persian text-to-speech (TTS) synthesis system. Thesyllables used are prosodically conditioned in the sense that a single conventional syllable is stored as different versions taken directly from the different prosodic domains of the prosodically labeled, read sentences. The three levels of the Persian prosodic hierarchy were observed in the syllable selection process, thereby selecting three different versions of each syllable from the prosodic domains of the intonational phrase (IP), accentual phrase (AP) and prosodic word (PW). A listening experiment designed to evaluate the quality of the syllable database showed that listeners preferred stimuli composed of prosodically appropriate diphones. We interpret this as supporting the view that segments carry prosodic domain information.}, Keywords = {concatenation-prosodically sensitive units- synthesis }, volume = {7}, Number = {2}, pages = {69-84}, publisher = {Research Center on Developing Advanced Technologies}, url = {http://jsdp.rcisp.ac.ir/article-1-715-en.html}, eprint = {http://jsdp.rcisp.ac.ir/article-1-715-en.pdf}, journal = {Signal and Data Processing}, issn = {2538-4201}, eissn = {2538-421X}, year = {2011} } @article{ author = {ZamaniDehkordi, Behzad and akbari, ahmad and Nasersharif, Babak}, title = {Feature transformation using kernel minimum classification error (KMCE) for pattern and speech recognition}, abstract ={As}, Keywords = {Feature Transformation, Linear Discriminant Analysis, Principal Component analysis, Minimum Classification Error, Kernel Function}, volume = {7}, Number = {1}, pages = {3-18}, publisher = {Research Center on Developing Advanced Technologies}, url = {http://jsdp.rcisp.ac.ir/article-1-723-en.html}, eprint = {http://jsdp.rcisp.ac.ir/article-1-723-en.pdf}, journal = {Signal and Data Processing}, issn = {2538-4201}, eissn = {2538-421X}, year = {2010} } @article{ author = {Moshki, Mohsen and Parvin, Hamid and MinaeiBidgoli, Behrouz}, title = {Clustering Ensemble based on combination of subset of primary clusters}, abstract ={Most of the recent studies have tried to create diversity in primary results and then applied a consensus function over all the obtained results to combine the weak partitions. In this paper a clustering ensemble method is proposed which is based on a subset of primary clusters. The main idea behind this method is using more stable clusters in the ensemble. The stability is applied as a goodness measure of the clusters. The clusters which satisfy a threshold of this measure are selected to participate in the ensemble. For combining the chosen clusters, a co-association based consensus function is applied. A new EAC based method which is called Extended Evidence Accumulation Clustering, EEAC, is proposed for constructing the Co-association Matrix from the subset of clusters. The proposed method is evaluated on five different UCI repository data sets. The empirical studies show the significant improvement of the proposed method in comparison with other ones.  }, Keywords = {Clustering Ensemble, Cluster Stability, Mutual Information, Co-association Matrix}, volume = {7}, Number = {1}, pages = {19-32}, publisher = {Research Center on Developing Advanced Technologies}, url = {http://jsdp.rcisp.ac.ir/article-1-725-en.html}, eprint = {http://jsdp.rcisp.ac.ir/article-1-725-en.pdf}, journal = {Signal and Data Processing}, issn = {2538-4201}, eissn = {2538-421X}, year = {2010} } @article{ author = {HabibiAghdam, Hame}, title = {Automatic Recognition of Music Genre}, abstract ={Nowadays, automatic analysis of music signals has gained a considerable importance due to the growing amount of music data found on the Web. Music genre classification is one of the interesting research areas in music information retrieval systems. In this paper several techniques were implemented and evaluated for music genre classification including feature extraction, feature selection and music genre modeling on a database of 8 different music genres containing Celtic, Classic, Classic Piano, Jazz, Metal, Persian Classic, Relaxing and Dance music. This database was gathered from several albums composed by different musicians. Short, middle and long term features were studied and finally only short and middle term features were used in our experiments. The long term features were discarded due to their low performance in music genre classification. Two modeling types of the music genres were evaluated. In the first type, only distribution of the feature vectors was used and in the second type, the ordering of the feature vectors was taken into account. Some modeling techniques such as ANN, GMM, Decision Tree and SVM were used individually and in a hierarchical approach. We proposed a taxonomy which classifies the music genres in a hierarchy where there are a small number of classes in the root and large number of classes in leaves. In fact, each class at the root of taxonomy contains one or more music genres and each genre is represented as a leaf at the bottom of the taxonomy. In addition, several classifiers were used simultaneously, in a way that each of them classifies the music genres individually. The decision is finally made using a voting algorithm. Besides, several short-term feature extraction techniques which have successfully been applied in speech recognition, music instrument classification and also music genre classification were studied and after analysis of the experimental results using statistical measures and different combinations of features, a near optimal feature vector was selected.  }, Keywords = {Automatic recognition of music genre, spectral feature, octave base spectral contrast, octave based signal intensities, mel frequency cepstral coefficients, feature integration, hierarchical classification}, volume = {7}, Number = {1}, pages = {33-52}, publisher = {Research Center on Developing Advanced Technologies}, url = {http://jsdp.rcisp.ac.ir/article-1-724-en.html}, eprint = {http://jsdp.rcisp.ac.ir/article-1-724-en.pdf}, journal = {Signal and Data Processing}, issn = {2538-4201}, eissn = {2538-421X}, year = {2010} } @article{ author = {}, title = {Localization of Multiple Speakers in Echoic Environments Using BSS and Speech Features for Solution of Global Permutation Ambiguity}, abstract ={In this paper, a new algorithm is introduced for localization of multiple speakers in echoic environments. The origin of localization is based on combination of TDOA estimates of each source obtained by the BSS algorithm in the time domain. A new BSS algorithm is proposed which improves the quality and channel identification compared to a reference technique and also reduces the computational cost in some cases. To solve the global permutation ambiguity of BSS algorithms, speech features are used. Simulation results show the effectiveness of these features for solving the later problem.}, Keywords = {Keywords:TDOA estimation, PSO optimization, blind source separation, global permutation.}, volume = {7}, Number = {1}, pages = {53-64}, publisher = {Research Center on Developing Advanced Technologies}, url = {http://jsdp.rcisp.ac.ir/article-1-729-en.html}, eprint = {http://jsdp.rcisp.ac.ir/article-1-729-en.pdf}, journal = {Signal and Data Processing}, issn = {2538-4201}, eissn = {2538-421X}, year = {2010} } @article{ author = {}, title = {Phrasal Verb Translation from English to Persian Using Statistical Parsing}, abstract ={Machine translation of English sentences faces a big problem when it deals with phrasal verbs. Phrasal verb is a common structure occurring in English as a combination of a verb and a preposition, a verb and an adverb, or a verb with both an adverb and a preposition. Meaning of a phrasal verb is not compositional. The second part of the phrasal verbs which often is a preposition is called particle. The process of detecting a preposition as a particle or as an attachment in a preposition phrase can be a challenging problem. In this paper, we present a method which uses a combination of linguistic heuristic rules with a probabilistic English parser to disambiguate the role of prepositions. The aim of this disambiguation is to correctly detect the phrasal verbs in English to Persian machine translation system. Experiments on a corpus containing 520 sentences show that the quality of phrasal verb recognition in this system grows up to 87%.}, Keywords = {Keywords: Machine Translation, Statistical Parsing, Phrasal Verb, Persian language}, volume = {7}, Number = {1}, pages = {65-76}, publisher = {Research Center on Developing Advanced Technologies}, url = {http://jsdp.rcisp.ac.ir/article-1-730-en.html}, eprint = {http://jsdp.rcisp.ac.ir/article-1-730-en.pdf}, journal = {Signal and Data Processing}, issn = {2538-4201}, eissn = {2538-421X}, year = {2010} } @article{ author = {}, title = {Persian name entity recognition and classification}, abstract ={Name entity recognition (NER) is a system that can identify one or more kinds of names in a text and classify them into specified categories. These categories can be name of people, organizations, companies, places (country, city, street, etc.), time related to names (date and time), financial values, percentages, etc. Although during the past decade a lot of researches has been done on NER in different languages, but lack of a system with admissible performance in Farsi texts is quietly sensible. In this paper, the Corpus of Research Center of Intelligent Signal Processing has been used to create a Farsi NER. In our proposed NER system, there exist three stages: preprocessing, feature extraction and classification. To prepare a data set in the preprocessing stage, by using the part of speech (POS) feature, names are extracted from text and then infinitives, time related names, counting names, and numbers are removed from data. This gives a more balanced data set for learning and classification. In the feature extraction stage, N-gram is computed as feature, and four classifiers (linear, KNN, Bayesian, Neural Network) is learned in the classification stage. Because of lack of variety in the time related names and a few number of mixture of time related names with names in the other categories, an auxiliary list is used to identifying them. The results of research show, neural network have better performance (99%) in distinct between the names of places and people. In general, KNN and linear classifiers obtain 91% success based on F-measure scale in classifying the names of places and people and general names. In classifying the time related names, using an auxiliary list, based on an F-measure scale, a 96% success was obtained.}, Keywords = {Keywords: Natural language processing, Name entity recognition, N-gram, Neural Network}, volume = {7}, Number = {1}, pages = {77-88}, publisher = {Research Center on Developing Advanced Technologies}, url = {http://jsdp.rcisp.ac.ir/article-1-731-en.html}, eprint = {http://jsdp.rcisp.ac.ir/article-1-731-en.pdf}, journal = {Signal and Data Processing}, issn = {2538-4201}, eissn = {2538-421X}, year = {2010} } @article{ author = {soryani,}, title = {Face Recognition by Combination of PCA and Gabor Filter}, abstract ={Methods for face recognition which are based on face structure are among techniques without supervision and produce unfavorable results in the presence of linear changes in images. PCA is a linear transform and a powerful tool for data analysis but does not produce good results for face recognition when there are non-linear changes resulting from changes in position, intensity and gesture in the face image. To overcome this problem, methods based on face features are used. Gabor filtering which can be considered as a feature based method can be used in these cases. This paper presents a new face recognition algorithm by combining PCA and Gabor filtering methods. After Gabor filtering of each face image, a number of images is produced. Then, mean of these images is calculated and PCA is applied to it. The resulted principal components are then used for face recognition. The presented algorithm has been applied to face images from YaleB and ORL databases under different conditions. Results show that the new algorithm performs better than PCA or Gabor filtering methods when they are applied to face images independently.}, Keywords = {Keywords: Face Recognition, Gabor filtering, PCA}, volume = {7}, Number = {1}, pages = {89-96}, publisher = {Research Center on Developing Advanced Technologies}, url = {http://jsdp.rcisp.ac.ir/article-1-732-en.html}, eprint = {http://jsdp.rcisp.ac.ir/article-1-732-en.pdf}, journal = {Signal and Data Processing}, issn = {2538-4201}, eissn = {2538-421X}, year = {2010} }