Speaker Identification and Verification Using Different Model for Text-Dependent
|
|
- Dorthy Mitchell
- 5 years ago
- Views:
Transcription
1 International Journal of Alied Engineering Research ISSN Volume 12, Number 8 (2017) Research India Publications. htt:// Seaker Identification and Verification Using Different Model for Text-Deendent 1 Shrikant Uadhyay, 2 Sudhir Kumar Sharma & 3 Aditi Uadhyay Deartment of Electronics & Communication Engineering, 1 Cambridge Institute of Technology, Ranchi, Jharkhand, India. 2, 3 Jaiur National University, Jaiur, Rajasthan, India. Abstract Human voice is one of the medium through which everybody communicate and share their exeriences, thought, feeling etc. and make it meaningful by understanding their meaning and actions. Issue is not that erson is seaking in front of about 10-20m and getting clear voice to the next erson sitting in front. The erson stays at far location and tries to access their various need alications like assword, PIN number, account access etc. with the hel of voice then it will be quite difficult to do so. Seaker is the medium through which we can transfer your voice in such occasion so, seaker identification and verification is very imortant. Text-deendent samle is quite easy to determine and verified by the erson sitting to the remote location as all information has rior saved to the database of unknown user and doesn t require any additional information. Different roosed model have been analyzed in terms of efficiency, loss and error using different feature extraction techniques in this aer. We ut your effort and try to find out that how the model behaves using different feature extraction combinations. INTRODUCTION Within the ast decade, technological growth advances remote collaborative data rocessing over huge comuter network, hands-free sound cature, telebanking have increased the demand for imroved method of information security. For ersonal information including credit history, bank details, medical records, the ability to verify the identity of individuals attemting to access such data is challenging and critical. To date, low-cost strategy such as magnetic cards, ersonal identification numbers (ins), asswords have been widely used. More advanced security measures have also been develoed (e.g., retinal scanners, face recognition, as well as automatic finger rint detectors). The use of these rocedures has been limited by both cost and ease of use. In recent years, seaker recognition (recognizing a erson from his/her voices by a machine) and verification algorithms have also received considerable attention. In articular, seech rovides a convenient and natural form of inut conveys a significant amount of seaker deendent information, and it is inexensive to collect and analyze. Voice is a henomenon that is highly deendent on the seaker who roduced it. Many hysical asects of seech such as the timber, tone or intensity vary a lot from a seaker to another. The same haens with other linguistic asects as the single intonation and range of vocabulary or exressions a seaker normally uses. All these roerties make voice a very owerful biometric key to be used in security systems since the hysical characteristics of seech are easy to measure and comare in comarison to other biometric keys. In addition, the seech signal is quite well-known and has been deely studied for many years so many owerful algorithms can be found to deal with this kind of signal. Text-deendent task involves some form of re-determined or romted assword, in order to obtain the required text. It can be used for alications such as voice mode assword or signature verification. In such alications, there is a need to change the assword frequently and it can be done easily by changing the re-determined text. In text-deendent seaker verification, during enrolment hase a limited number of utterances of the fixed text are collected. Therefore, aroaches based on temlate matching are used for attern comarison instead of aroaches based on statistics or artificial neural networks, which needs a large amount of training data. Research in seech rocessing and communication, for the most art, was motivated by eole s desire to build mechanicals models to emulate human verbal communication. Research interest in seech rocessing today has done well beyond the notion of mimicking human vocal aaratus [1]. Seech is the rimary communication medium between eole. This communication rocess has a comlex structure consisting not only of the transmission of voice but also includes transmission of many seaker secific information. Therefore, from the last five decades eole have come forward to investigate various asects of seech such as mechanical realization of seech signal, human machine interaction and seech & seaker recognition. Seech is a biometric that combines hysiological and behavioral characteristics. It is articularly useful for remote-access transactions over telecommunication networks. Presently this task is the most challenging one to researchers. Peole can reliably identify familiar voices and about 2-3 seconds is enough to identify a voice, although erformance decreases for unfamiliar voices [2]. Even if duration of the utterance was increased, but layed backward (which distorts timing and articulatory cues), the accuracy decreases drastically. Widely 1633
2 International Journal of Alied Engineering Research ISSN Volume 12, Number 8 (2017) Research India Publications. htt:// varying erformance on this background task suggested that cues to voice recognition vary from voice to voice, so that voice atterns may consist of a set of acoustic cues from which listeners select a subset to use in identifying individual voices. IDENTIFICATION & VERIFICATION Seaker recognition by a machine involves three stages. They are: (1) Extraction of features to reresent the seaker information resent in the seech signal. (2) Modelling of seaker features. (3) Decision logic to imlement the identification or verification task. The rimary task in a seaker recognition system is to extract features caable of reresenting the seaker information resent in the seech signal. It is known to us that human beings use high-level features such as style of seech, seech dialect and verbal mannerisms (for examle, a articular kind of a laugh or use of articular idioms and words) to recognize seakers. Intuitively, it is clear that these features constitute imortant seaker information. Difficulty arises due to limitations of the existing feature extraction techniques [3]. Current seaker recognition systems follow segmental features such as the shae of the vocal tract to reresent the seaker-secific information. These features show significant variations across seakers, but they also show considerable variations from time to time for a single seaker. In addition to this, the characteristics of the recording equiment and transmission channel are also reflected in these features [3]. Once a roer set of feature vector is obtained, then in the next hase task in seaker recognition is to develo a model (rototye) for each seaker. The develoment of seaker modelling is called the training hase. The block diagram of training hase is shown in Figure 1. Figure 2. Testing in seaker recognition system In seaker identification a seech utterance from an unknown seaker is analyzed and comared with models of all known seakers. The unknown seaker is identified as the seaker whose model best matches the inut utterance. Figure 3 shows the basic structure of seaker identification system. Seaker identification can be a closed set identification or an oen set identification. In a closed set identification, it is assumed that the test utterance belongs to one of N enrolled seakers (N decisions). In the case of oen set identification, there is an additional decision to be made to determine whether the test utterance was uttered by one of the N enrolled seakers or not, that is, there are N+1 decision levels. Figure 1. Training in seaker recognition system Feature vectors reresenting the voice characteristics of the seaker are extracted and used for building the reference models. The erformance of a seaker recognition system deends rimarily on the effectiveness of the models in caturing the seaker-secific information, and hence this hase lays a major role in determining the erformance of a seaker recognition system. The final stage in the develoment of a seaker recognition system is the decision logic stage, where a decision to either accet or reject the claim of a seaker is taken based on the result of matching techniques used. The block diagram of decision logic and testing hase rocess is shown in the Figure 2. Figure 3. Seaker verification system FEATURE EXTRACTION METHODS Feature extraction involved in signal modeling that erforms temoral and sectral analysis. The need of feature extraction arises because the raw seech signal contains information to convey message to the observer or receiver and has a high dimensionality. Feature extraction algorithm derives a 1634
3 International Journal of Alied Engineering Research ISSN Volume 12, Number 8 (2017) Research India Publications. htt:// characteristics feature vector with lower hysical or satial roerties. A. Mel-Scale Cestrum Co-efficient (MFCC) MFCC technique is basically used to generate the fingerrints of the audio files. Let us consider each frame consist of N samles and let its adjacent frames be searated by M samles where M is less than N. Hamming window is used in which each frame is multilied. Mathematically, Hamming window equation is given by: W (n) = cos( 2πn N 1 ) (1) Now, Fourier Transform (FT) is used to convert the signal from time domain to its frequency domain. Mathematically, it is given by: N 1 X k = x i e 2πik N 1 i=0 (2) M = 2595log 10 (1+ f 700 ) (3) In the next ste log Mel scale sectrum is converted to time domain using Discrete Cosine Transform (DCT). Mathematically, DCT is defined as follow: N 1 X k = α i=0 x i (2i + 1/2N) (4) The result of the conversion is known as MFCC and the set of co-efficient is called acoustic vectors. B. Linear Predictive Coding Analysis (LPC) It is a frame based analysis of the seech signal erformed to rovide observation vectors [3]. The relation between seech samle S (n) and excitation X(n) for auto regressive model (system assume all ole mode) is exlained mathematically as: S (n) = k=1 a k s (n-k) + G. X (n) (5) The system function is defined as: H (z) = S(Z) X(Z) A linear redictor of order with rediction co-efficient (α k) is defined as a system whose outut is defined as: (6) s (n) = k=1 α k S (n-k) (7) The system function is th order olynomial and it follows: P (z) = α k z k (8) The rediction error e (n) is defined as: e (n) = s(n) - s (n) = s (n) - k=1 α k S (n-k) (9) The transfer function of rediction error sequence is: A (z) = 1 - k=1 α k z k (10) Now, by comaring equation (5) and (10), if α k = α k then A (z) will be inverse filter for the system H (z) of equation (6): H (z) = G/ A (z) (11) The urose is to find out set of redictor coefficients that will minimize the mean squared error over a short segment of seech waveform. So, short-time average rediction error is defined as [4]. E (n) = m (e n (m)) 2 k 1 } = m {s n (m) α k s n (m k) (12) where, s n (m) is segment of seech in surrounding of n samles i.e. s n (m) = s (n + m) Now, the value of α k minimize E n are obtained by taking E n / α i = 0 & i = 0, 1, 2... thus getting the equation: m s n (m i)s n (m) = k 1 α k s n (m i)s n (m k) (13) If n (i, k) = m s n (m i)s n (m k) (14) Thus, equation (13) rewritten as: k=1 α k φ k (i, k) =φ k (i, 0), for i= 1, 2, 3... (15) The three ways available to solve above equation i.e. autocorrelation method, lattice method and covariance method. In seech recognition the autocorrelation is widely used because of its comutational efficiency and inherent stability [4]. Seech segment is windowed in autocorrelation method as discuss below: S n = S (m + n) + w (m) for 0 m N-1 (16) Where, w (m) is finite window length Then, we have φ n (i, k) = N+ 1 m=0 s n (m i)s n (m k) for 1 i, 0 k (17) φ n (i, k) = R n (i k) (18) Where, R n (k) = N 1 k m=0 s n (m) s n (m + k) R k (k) is autocorrelation function then equation (15) is simlified as [5] k 1 α k R n ( i k ) = R i (i) for 1 i (19) Thus using Durbin s recursive rocedure the resulting equation is solved as: E (i) = (1- k i 2 ) E (i-1) (20) Then from equation (19) to (22) are solved recursively for i = 1, 2... and this give final equation as: 1635
4 International Journal of Alied Engineering Research ISSN Volume 12, Number 8 (2017) Research India Publications. htt:// () α j = LPC coefficient = α j k i = PACOR coefficients A very essential LPC arameter set which is derived directly from LPC coefficients is LPC ceestral coefficients C m. The recursion used for this discussed as [6]: C 0 = ln G (21) structures known as recognizer outut voting error rate. This method treats the outut of multile ASR systems as indeendent knowledge sources, and then combines these oututs to roduce a comosite outut with a lower word error rate than an individual ASR outut. It roceeds in two stages as shown in Figure 5. m 1 C m = α m + ( k k=1 ) C m k a m k for 1 m (22) m 1 C m = ( k k=1 ) C m k a m k for m > (23) C. Linear Prediction Ceestral Coefficients (LPCC) The basic arameters for estimating a seech signal, LPCC lay a very dominant role. This method is that where one seech samle at the current time can be redicated as a linear combination of ast seech sequence or samle. LPCC algorithm in term of block diagram is shown in Figure 4. below: Figure 5. Proosed model (ROVER based) The second roosed model is based on combined combination known as CNC as shown in Figure 6. below aroach to seech recognition, the decoder oututs the string of word hyotheses corresonding to the ath with highest osterior robability given the acoustic and a language model, and reresented by either N-best lists or word lattice. Figure 4. LPCC algorithm rocess RELATED WORK This aer focus on the Hindi database text-deendent samle soken from SHUNAY (0) to NAU (9). This database is reared using GoldWave software using version This samle is tested using MATLAB tool for their efficiency and error rate. The roosed model is tested using various combinations of feature extraction techniques like, and MFCC+LPC. The considered arameters for analysis are mention in Table 1. Table 1: Parameters used for analysis Parameters Values Total number of seakers 24 Single channel Samling rate Language PROPOSED MODEL 16KHz 16 bit (For less quantization error) Hindi (Phonetics is rich) The roosed model designed based on the aims to achieve better erformance by exloiting differences in the nature of the errors occurred at the level of different modeling Figure 6. Proosed model (CNC based) These two models have been used to calculate the efficiency and error rate of the seech signal soken from SHUNYA (0) to NAU (9). And with the hel of this model we exactly know that which model erforms better also that which combinations of feature extraction techniques will better for both the model. Earlier no such models have been develo 1636
5 International Journal of Alied Engineering Research ISSN Volume 12, Number 8 (2017) Research India Publications. htt:// using such combination. So, it is quite useful for seaker recognition. RESULTS The simulation has been done using MATLAB tool also taking hel with COOL and GoldWave software to analyze the database. Effeciency (%) Dialects Figure 7. Efficiency for ROVER model MFCC+LPC Error rate (%) Calculations: Figure 10. Error rate for CNC model GMMs (Gaussian Mixture Models) are trained searately on each seaker s enrolment data using the Exectation Maximization (EM) algorithm [255]. The udate equations that guarantee a monotonic increase in the model s likelihood value are: Mixture weight: Dialects T MFCC+LPC i = 1 (i x T t=1 t,λ) (24) Error rate (%) Dialects MFCC+LPCC Means: Variance: μ i = σ i Recognition Rate = 2 = T t 1 T t 1 T t 1 (i x t,λ) x t, (i x t,λ) (i x t,λ) 2 xt T t 1 (i x t,λ) Successfully detected words Number of words in test set (25) - μ 2 i (26) (27) Effeciency (%) Figure 8. Error rate for ROVER model MFCC+LPCC Exeriments were erformed with different tyes of seech, that is, for slow seech (less than 12 hones er second), for normal seech (12 to 18 hones er second) and for fast seech (more than 18 hones er second). Maximum accuracy is achieved for normal seech as shown in Figure 11. Figure 9. Efficiency for CNC model Figure11. Seech rate versus accuracy 1637
6 International Journal of Alied Engineering Research ISSN Volume 12, Number 8 (2017) Research India Publications. htt:// CONCLUSION & FUTURE SCOPE Efficiency rate of MFCC+LPC for ROVER model is 98.22% comared to and i.e % and Error rate of MFCC+LPC is quite low i.e. 1.22% comared to and i.e. 3.53% and 4.42%. On the other hand efficiency rate of for CNC model is 98.99% comared to and MFCC+LPCC i.e and Error rate of here is 1.29% comared to and MFCC+LPCC i.e and So, with the hel of above calculation and analysis we can choose ROVER model with the feature extraction combination of MFCC+LPC for getting good and efficient seech recognition. For CNC based model we can have good and efficient combination of which give good efficiency and less error rate comared to other two combinations and may be useful for accurate seaker recognition. The combination may be used for different tyes of model taking the acoustic condition in future. ACKNOWLEDGMENT We are extremely thankful to the Prof. (Dr.) Sudhir Kumar Sharma (HOD, Deuty Director, Jaiur National University), and Prof. (Dr.) Pawan Kumar (HOD, Cambridge Institute of Technology) for their technical suort and guidance. AUTHORS PROFILE: Mr. Shrikant Uadhyay, Research Scholar at Jaiur National University, Jaiur. He received his M.Tech degree from Dehradun Institute of Technology (University) in His current research area includes seech rocessing, image rocessing, neural network and the security challenges for seaker identification and verification in signal rocessing domain. Dr. Sudhir Kumar Sharma is Professor & Head at the Deartment of Electronics & Communication Engineering, School of Engineering and Technology, Jaiur National University, Jaiur, Rajasthan, India. Professor Sharma received his Ph.D. in Electronics from Delhi University in Professor Sharma has an extensive teaching exerience of 19 years. He has been keenly carrying out research activities since last 20 years rominently in the field of Otical Communication Signal Processing. Mrs. Aditi Uadhyay, Research Scholar. She received M.Tech degree from Dehradun Institute of Technology (University) in Her current research area includes seech rocessing, image rocessing using different state of HMM. Image enhancement by using different acoustic model. REFERENCES [1] D. G. Childers, R. V. Cox, R. Demori, B. H. Juang, J. J. Mariani, P. Price, S. Sagayama, M. M. Sondhi and R. Weischedel, The ast, resent and future of seech rocessing, IEEE Processing Magazine, , May [2] D. Lancker, J. Kreiman and K. Emmorey, Familiar voice recognition: Pattern and arametersrecognition of backward voices, Phonetics, vol. 13, no. 1, , [3] Abdelnaiem, LPC and MFCC erformance evaluation with artificial neural network for soken language identification, International Journal of Signal Processing, Image Processing and Pattern Recognition, vol. 6, , June [4] L.R. Rabiner and R.W. Schafer, Digital rocessing of seech signals. Englewood cliffs, New Jersey: Prentice-Hall, [5] L.R. Rabiner and B.H. Juang, Fundamental of seech recognition, Englewood cliffs, New Jersey: Prentice- Hall, [6] Han Y, Wang G and Y Yang, Seech emotion recognition based on mfcc, Journal of Chong Qing. University of Posts and Telecommunication (Natural Science Ediion) vol.69, ,
Keywords: Vocal Tract; Lattice model; Reflection coefficients; Linear Prediction; Levinson algorithm.
Volume 3, Issue 6, June 213 ISSN: 2277 128X International Journal of Advanced Research in Comuter Science and Software Engineering Research Paer Available online at: www.ijarcsse.com Lattice Filter Model
More informationAutoregressive (AR) Modelling
Autoregressive (AR) Modelling A. Uses of AR Modelling () Alications (a) Seech recognition and coding (storage) (b) System identification (c) Modelling and recognition of sonar, radar, geohysical signals
More informationState Estimation with ARMarkov Models
Deartment of Mechanical and Aerosace Engineering Technical Reort No. 3046, October 1998. Princeton University, Princeton, NJ. State Estimation with ARMarkov Models Ryoung K. Lim 1 Columbia University,
More information4. Score normalization technical details We now discuss the technical details of the score normalization method.
SMT SCORING SYSTEM This document describes the scoring system for the Stanford Math Tournament We begin by giving an overview of the changes to scoring and a non-technical descrition of the scoring rules
More informationLPC methods are the most widely used in. recognition, speaker recognition and verification
Digital Seech Processing Lecture 3 Linear Predictive Coding (LPC)- Introduction LPC Methods LPC methods are the most widely used in seech coding, seech synthesis, seech recognition, seaker recognition
More informationCombining Logistic Regression with Kriging for Mapping the Risk of Occurrence of Unexploded Ordnance (UXO)
Combining Logistic Regression with Kriging for Maing the Risk of Occurrence of Unexloded Ordnance (UXO) H. Saito (), P. Goovaerts (), S. A. McKenna (2) Environmental and Water Resources Engineering, Deartment
More informationDistributed Rule-Based Inference in the Presence of Redundant Information
istribution Statement : roved for ublic release; distribution is unlimited. istributed Rule-ased Inference in the Presence of Redundant Information June 8, 004 William J. Farrell III Lockheed Martin dvanced
More informationHotelling s Two- Sample T 2
Chater 600 Hotelling s Two- Samle T Introduction This module calculates ower for the Hotelling s two-grou, T-squared (T) test statistic. Hotelling s T is an extension of the univariate two-samle t-test
More informationA Comparison between Biased and Unbiased Estimators in Ordinary Least Squares Regression
Journal of Modern Alied Statistical Methods Volume Issue Article 7 --03 A Comarison between Biased and Unbiased Estimators in Ordinary Least Squares Regression Ghadban Khalaf King Khalid University, Saudi
More informationNotes on Instrumental Variables Methods
Notes on Instrumental Variables Methods Michele Pellizzari IGIER-Bocconi, IZA and frdb 1 The Instrumental Variable Estimator Instrumental variable estimation is the classical solution to the roblem of
More informationChapter 7 Sampling and Sampling Distributions. Introduction. Selecting a Sample. Introduction. Sampling from a Finite Population
Chater 7 and s Selecting a Samle Point Estimation Introduction to s of Proerties of Point Estimators Other Methods Introduction An element is the entity on which data are collected. A oulation is a collection
More informationEvaluating Circuit Reliability Under Probabilistic Gate-Level Fault Models
Evaluating Circuit Reliability Under Probabilistic Gate-Level Fault Models Ketan N. Patel, Igor L. Markov and John P. Hayes University of Michigan, Ann Arbor 48109-2122 {knatel,imarkov,jhayes}@eecs.umich.edu
More informationLower Confidence Bound for Process-Yield Index S pk with Autocorrelated Process Data
Quality Technology & Quantitative Management Vol. 1, No.,. 51-65, 15 QTQM IAQM 15 Lower onfidence Bound for Process-Yield Index with Autocorrelated Process Data Fu-Kwun Wang * and Yeneneh Tamirat Deartment
More informationarxiv: v1 [physics.data-an] 26 Oct 2012
Constraints on Yield Parameters in Extended Maximum Likelihood Fits Till Moritz Karbach a, Maximilian Schlu b a TU Dortmund, Germany, moritz.karbach@cern.ch b TU Dortmund, Germany, maximilian.schlu@cern.ch
More informationON THE USE OF PHASE INFORMATION FOR SPEECH RECOGNITION. Baris Bozkurt and Laurent Couvreur
ON THE USE OF PHASE NFOMATON FO SPEECH ECOGNTON Baris Bozkurt and Laurent Couvreur TCTS Lab, Faculté Polytechnique De Mons, nitialis Scientific Park, B-7000 Mons, Belgium, hone: +32 65 374733, fax: +32
More informationFeedback-error control
Chater 4 Feedback-error control 4.1 Introduction This chater exlains the feedback-error (FBE) control scheme originally described by Kawato [, 87, 8]. FBE is a widely used neural network based controller
More informationRobust Speaker Identification
Robust Speaker Identification by Smarajit Bose Interdisciplinary Statistical Research Unit Indian Statistical Institute, Kolkata Joint work with Amita Pal and Ayanendranath Basu Overview } } } } } } }
More informationThe Weighted Sum of the Line Spectrum Pair for Noisy Speech
HELSINKI UNIVERSITY OF TECHNOLOGY Deartment of Electrical and Communications Engineering Laboratory of Acoustics and Audio Signal Processing Zhijian Yuan The Weighted Sum of the Line Sectrum Pair for Noisy
More informationSeafloor Reflectivity A Test of an Inversion Technique
Seafloor Reflectivity A Test of an Inversion Technique Adrian D. Jones 1, Justin Hoffman and Paul A. Clarke 1 1 Defence Science and Technology Organisation, Australia, Student at Centre for Marine Science
More informationENHANCING TIMBRE MODEL USING MFCC AND ITS TIME DERIVATIVES FOR MUSIC SIMILARITY ESTIMATION
th Euroean Signal Processing Conference (EUSIPCO ) Bucharest, Romania, August 7-3, ENHANCING TIMBRE MODEL USING AND ITS TIME DERIVATIVES FOR MUSIC SIMILARITY ESTIMATION Franz de Leon, Kirk Martinez Electronics
More informationAI*IA 2003 Fusion of Multiple Pattern Classifiers PART III
AI*IA 23 Fusion of Multile Pattern Classifiers PART III AI*IA 23 Tutorial on Fusion of Multile Pattern Classifiers by F. Roli 49 Methods for fusing multile classifiers Methods for fusing multile classifiers
More informationA Recursive Block Incomplete Factorization. Preconditioner for Adaptive Filtering Problem
Alied Mathematical Sciences, Vol. 7, 03, no. 63, 3-3 HIKARI Ltd, www.m-hiari.com A Recursive Bloc Incomlete Factorization Preconditioner for Adative Filtering Problem Shazia Javed School of Mathematical
More informationThe Noise Power Ratio - Theory and ADC Testing
The Noise Power Ratio - Theory and ADC Testing FH Irons, KJ Riley, and DM Hummels Abstract This aer develos theory behind the noise ower ratio (NPR) testing of ADCs. A mid-riser formulation is used for
More informationSpectral Analysis by Stationary Time Series Modeling
Chater 6 Sectral Analysis by Stationary Time Series Modeling Choosing a arametric model among all the existing models is by itself a difficult roblem. Generally, this is a riori information about the signal
More informationNamed Entity Recognition using Maximum Entropy Model SEEM5680
Named Entity Recognition using Maximum Entroy Model SEEM5680 Named Entity Recognition System Named Entity Recognition (NER): Identifying certain hrases/word sequences in a free text. Generally it involves
More informationPER-PATCH METRIC LEARNING FOR ROBUST IMAGE MATCHING. Sezer Karaoglu, Ivo Everts, Jan C. van Gemert, and Theo Gevers
PER-PATCH METRIC LEARNING FOR ROBUST IMAGE MATCHING Sezer Karaoglu, Ivo Everts, Jan C. van Gemert, and Theo Gevers Intelligent Systems Lab, Amsterdam, University of Amsterdam, 1098 XH Amsterdam, The Netherlands
More informationMachine Learning: Homework 4
10-601 Machine Learning: Homework 4 Due 5.m. Monday, February 16, 2015 Instructions Late homework olicy: Homework is worth full credit if submitted before the due date, half credit during the next 48 hours,
More informationUncorrelated Multilinear Principal Component Analysis for Unsupervised Multilinear Subspace Learning
TNN-2009-P-1186.R2 1 Uncorrelated Multilinear Princial Comonent Analysis for Unsuervised Multilinear Subsace Learning Haiing Lu, K. N. Plataniotis and A. N. Venetsanooulos The Edward S. Rogers Sr. Deartment
More informationCOMPARISON OF VARIOUS OPTIMIZATION TECHNIQUES FOR DESIGN FIR DIGITAL FILTERS
NCCI 1 -National Conference on Comutational Instrumentation CSIO Chandigarh, INDIA, 19- March 1 COMPARISON OF VARIOUS OPIMIZAION ECHNIQUES FOR DESIGN FIR DIGIAL FILERS Amanjeet Panghal 1, Nitin Mittal,Devender
More informationAn Improved Calibration Method for a Chopped Pyrgeometer
96 JOURNAL OF ATMOSPHERIC AND OCEANIC TECHNOLOGY VOLUME 17 An Imroved Calibration Method for a Choed Pyrgeometer FRIEDRICH FERGG OtoLab, Ingenieurbüro, Munich, Germany PETER WENDLING Deutsches Forschungszentrum
More informationPairwise active appearance model and its application to echocardiography tracking
Pairwise active aearance model and its alication to echocardiograhy tracking S. Kevin Zhou 1, J. Shao 2, B. Georgescu 1, and D. Comaniciu 1 1 Integrated Data Systems, Siemens Cororate Research, Inc., Princeton,
More informationRUN-TO-RUN CONTROL AND PERFORMANCE MONITORING OF OVERLAY IN SEMICONDUCTOR MANUFACTURING. 3 Department of Chemical Engineering
Coyright 2002 IFAC 15th Triennial World Congress, Barcelona, Sain RUN-TO-RUN CONTROL AND PERFORMANCE MONITORING OF OVERLAY IN SEMICONDUCTOR MANUFACTURING C.A. Bode 1, B.S. Ko 2, and T.F. Edgar 3 1 Advanced
More informationOptimal Recognition Algorithm for Cameras of Lasers Evanescent
Otimal Recognition Algorithm for Cameras of Lasers Evanescent T. Gaudo * Abstract An algorithm based on the Bayesian aroach to detect and recognise off-axis ulse laser beams roagating in the atmoshere
More informationFormal Modeling in Cognitive Science Lecture 29: Noisy Channel Model and Applications;
Formal Modeling in Cognitive Science Lecture 9: and ; ; Frank Keller School of Informatics University of Edinburgh keller@inf.ed.ac.uk Proerties of 3 March, 6 Frank Keller Formal Modeling in Cognitive
More informationCHAPTER 5 STATISTICAL INFERENCE. 1.0 Hypothesis Testing. 2.0 Decision Errors. 3.0 How a Hypothesis is Tested. 4.0 Test for Goodness of Fit
Chater 5 Statistical Inference 69 CHAPTER 5 STATISTICAL INFERENCE.0 Hyothesis Testing.0 Decision Errors 3.0 How a Hyothesis is Tested 4.0 Test for Goodness of Fit 5.0 Inferences about Two Means It ain't
More informationInformation collection on a graph
Information collection on a grah Ilya O. Ryzhov Warren Powell February 10, 2010 Abstract We derive a knowledge gradient olicy for an otimal learning roblem on a grah, in which we use sequential measurements
More informationRANDOM WALKS AND PERCOLATION: AN ANALYSIS OF CURRENT RESEARCH ON MODELING NATURAL PROCESSES
RANDOM WALKS AND PERCOLATION: AN ANALYSIS OF CURRENT RESEARCH ON MODELING NATURAL PROCESSES AARON ZWIEBACH Abstract. In this aer we will analyze research that has been recently done in the field of discrete
More informationMODELING THE RELIABILITY OF C4ISR SYSTEMS HARDWARE/SOFTWARE COMPONENTS USING AN IMPROVED MARKOV MODEL
Technical Sciences and Alied Mathematics MODELING THE RELIABILITY OF CISR SYSTEMS HARDWARE/SOFTWARE COMPONENTS USING AN IMPROVED MARKOV MODEL Cezar VASILESCU Regional Deartment of Defense Resources Management
More informationPrinciples of Computed Tomography (CT)
Page 298 Princiles of Comuted Tomograhy (CT) The theoretical foundation of CT dates back to Johann Radon, a mathematician from Vienna who derived a method in 1907 for rojecting a 2-D object along arallel
More informationMinimax Design of Nonnegative Finite Impulse Response Filters
Minimax Design of Nonnegative Finite Imulse Resonse Filters Xiaoing Lai, Anke Xue Institute of Information and Control Hangzhou Dianzi University Hangzhou, 3118 China e-mail: laix@hdu.edu.cn; akxue@hdu.edu.cn
More informationChapter 2 Introductory Concepts of Wave Propagation Analysis in Structures
Chater 2 Introductory Concets of Wave Proagation Analysis in Structures Wave roagation is a transient dynamic henomenon resulting from short duration loading. Such transient loadings have high frequency
More informationShadow Computing: An Energy-Aware Fault Tolerant Computing Model
Shadow Comuting: An Energy-Aware Fault Tolerant Comuting Model Bryan Mills, Taieb Znati, Rami Melhem Deartment of Comuter Science University of Pittsburgh (bmills, znati, melhem)@cs.itt.edu Index Terms
More informationA MIXED CONTROL CHART ADAPTED TO THE TRUNCATED LIFE TEST BASED ON THE WEIBULL DISTRIBUTION
O P E R A T I O N S R E S E A R C H A N D D E C I S I O N S No. 27 DOI:.5277/ord73 Nasrullah KHAN Muhammad ASLAM 2 Kyung-Jun KIM 3 Chi-Hyuck JUN 4 A MIXED CONTROL CHART ADAPTED TO THE TRUNCATED LIFE TEST
More informationJohn Weatherwax. Analysis of Parallel Depth First Search Algorithms
Sulementary Discussions and Solutions to Selected Problems in: Introduction to Parallel Comuting by Viin Kumar, Ananth Grama, Anshul Guta, & George Karyis John Weatherwax Chater 8 Analysis of Parallel
More informationEnsemble Forecasting the Number of New Car Registrations
Ensemble Forecasting the Number of New Car Registrations SJOERT FLEURKE Radiocommunications Agency Netherlands Emmasingel 1, 9700 AL, Groningen THE NETHERLANDS sjoert.fleurke@agentschatelecom.nl htt://www.agentschatelecom.nl
More informationTests for Two Proportions in a Stratified Design (Cochran/Mantel-Haenszel Test)
Chater 225 Tests for Two Proortions in a Stratified Design (Cochran/Mantel-Haenszel Test) Introduction In a stratified design, the subects are selected from two or more strata which are formed from imortant
More informationMetrics Performance Evaluation: Application to Face Recognition
Metrics Performance Evaluation: Alication to Face Recognition Naser Zaeri, Abeer AlSadeq, and Abdallah Cherri Electrical Engineering Det., Kuwait University, P.O. Box 5969, Safat 6, Kuwait {zaery, abeer,
More informationInformation collection on a graph
Information collection on a grah Ilya O. Ryzhov Warren Powell October 25, 2009 Abstract We derive a knowledge gradient olicy for an otimal learning roblem on a grah, in which we use sequential measurements
More information1 University of Edinburgh, 2 British Geological Survey, 3 China University of Petroleum
Estimation of fluid mobility from frequency deendent azimuthal AVO a synthetic model study Yingrui Ren 1*, Xiaoyang Wu 2, Mark Chaman 1 and Xiangyang Li 2,3 1 University of Edinburgh, 2 British Geological
More informationSPECTRAL ANALYSIS OF GEOPHONE SIGNAL USING COVARIANCE METHOD
International Journal of Pure and Alied Mathematics Volume 114 No. 1 17, 51-59 ISSN: 1311-88 (rinted version); ISSN: 1314-3395 (on-line version) url: htt://www.ijam.eu Secial Issue ijam.eu SPECTRAL ANALYSIS
More informationModeling and Estimation of Full-Chip Leakage Current Considering Within-Die Correlation
6.3 Modeling and Estimation of Full-Chi Leaage Current Considering Within-Die Correlation Khaled R. eloue, Navid Azizi, Farid N. Najm Deartment of ECE, University of Toronto,Toronto, Ontario, Canada {haled,nazizi,najm}@eecg.utoronto.ca
More informationDeriving Indicator Direct and Cross Variograms from a Normal Scores Variogram Model (bigaus-full) David F. Machuca Mory and Clayton V.
Deriving ndicator Direct and Cross Variograms from a Normal Scores Variogram Model (bigaus-full) David F. Machuca Mory and Clayton V. Deutsch Centre for Comutational Geostatistics Deartment of Civil &
More informationBrownian Motion and Random Prime Factorization
Brownian Motion and Random Prime Factorization Kendrick Tang June 4, 202 Contents Introduction 2 2 Brownian Motion 2 2. Develoing Brownian Motion.................... 2 2.. Measure Saces and Borel Sigma-Algebras.........
More informationRadial Basis Function Networks: Algorithms
Radial Basis Function Networks: Algorithms Introduction to Neural Networks : Lecture 13 John A. Bullinaria, 2004 1. The RBF Maing 2. The RBF Network Architecture 3. Comutational Power of RBF Networks 4.
More informationElliptic Curves and Cryptography
Ellitic Curves and Crytograhy Background in Ellitic Curves We'll now turn to the fascinating theory of ellitic curves. For simlicity, we'll restrict our discussion to ellitic curves over Z, where is a
More informationCHAPTER-II Control Charts for Fraction Nonconforming using m-of-m Runs Rules
CHAPTER-II Control Charts for Fraction Nonconforming using m-of-m Runs Rules. Introduction: The is widely used in industry to monitor the number of fraction nonconforming units. A nonconforming unit is
More informationRecent Developments in Multilayer Perceptron Neural Networks
Recent Develoments in Multilayer Percetron eural etworks Walter H. Delashmit Lockheed Martin Missiles and Fire Control Dallas, Texas 75265 walter.delashmit@lmco.com walter.delashmit@verizon.net Michael
More informationAn Analysis of TCP over Random Access Satellite Links
An Analysis of over Random Access Satellite Links Chunmei Liu and Eytan Modiano Massachusetts Institute of Technology Cambridge, MA 0239 Email: mayliu, modiano@mit.edu Abstract This aer analyzes the erformance
More informationA Qualitative Event-based Approach to Multiple Fault Diagnosis in Continuous Systems using Structural Model Decomposition
A Qualitative Event-based Aroach to Multile Fault Diagnosis in Continuous Systems using Structural Model Decomosition Matthew J. Daigle a,,, Anibal Bregon b,, Xenofon Koutsoukos c, Gautam Biswas c, Belarmino
More informationResearch of power plant parameter based on the Principal Component Analysis method
Research of ower lant arameter based on the Princial Comonent Analysis method Yang Yang *a, Di Zhang b a b School of Engineering, Bohai University, Liaoning Jinzhou, 3; Liaoning Datang international Jinzhou
More informationEvaluating Process Capability Indices for some Quality Characteristics of a Manufacturing Process
Journal of Statistical and Econometric Methods, vol., no.3, 013, 105-114 ISSN: 051-5057 (rint version), 051-5065(online) Scienress Ltd, 013 Evaluating Process aability Indices for some Quality haracteristics
More informationCharacterizing the Behavior of a Probabilistic CMOS Switch Through Analytical Models and Its Verification Through Simulations
Characterizing the Behavior of a Probabilistic CMOS Switch Through Analytical Models and Its Verification Through Simulations PINAR KORKMAZ, BILGE E. S. AKGUL and KRISHNA V. PALEM Georgia Institute of
More informationResearch on Evaluation Method of Organization s Performance Based on Comparative Advantage Characteristics
Vol.1, No.10, Ar 01,.67-7 Research on Evaluation Method of Organization s Performance Based on Comarative Advantage Characteristics WEN Xin 1, JIA Jianfeng and ZHAO Xi nan 3 Abstract It as under the guidance
More informationMultilayer Perceptron Neural Network (MLPs) For Analyzing the Properties of Jordan Oil Shale
World Alied Sciences Journal 5 (5): 546-552, 2008 ISSN 1818-4952 IDOSI Publications, 2008 Multilayer Percetron Neural Network (MLPs) For Analyzing the Proerties of Jordan Oil Shale 1 Jamal M. Nazzal, 2
More informationMATH 2710: NOTES FOR ANALYSIS
MATH 270: NOTES FOR ANALYSIS The main ideas we will learn from analysis center around the idea of a limit. Limits occurs in several settings. We will start with finite limits of sequences, then cover infinite
More informationIndirect Rotor Field Orientation Vector Control for Induction Motor Drives in the Absence of Current Sensors
Indirect Rotor Field Orientation Vector Control for Induction Motor Drives in the Absence of Current Sensors Z. S. WANG *, S. L. HO ** * College of Electrical Engineering, Zhejiang University, Hangzhou
More informationTime Domain Calculation of Vortex Induced Vibration of Long-Span Bridges by Using a Reduced-order Modeling Technique
2017 2nd International Conference on Industrial Aerodynamics (ICIA 2017) ISBN: 978-1-60595-481-3 Time Domain Calculation of Vortex Induced Vibration of Long-San Bridges by Using a Reduced-order Modeling
More informationOutline. Markov Chains and Markov Models. Outline. Markov Chains. Markov Chains Definitions Huizhen Yu
and Markov Models Huizhen Yu janey.yu@cs.helsinki.fi Det. Comuter Science, Univ. of Helsinki Some Proerties of Probabilistic Models, Sring, 200 Huizhen Yu (U.H.) and Markov Models Jan. 2 / 32 Huizhen Yu
More informationChapter 1 Fundamentals
Chater Fundamentals. Overview of Thermodynamics Industrial Revolution brought in large scale automation of many tedious tasks which were earlier being erformed through manual or animal labour. Inventors
More informationProbability Estimates for Multi-class Classification by Pairwise Coupling
Probability Estimates for Multi-class Classification by Pairwise Couling Ting-Fan Wu Chih-Jen Lin Deartment of Comuter Science National Taiwan University Taiei 06, Taiwan Ruby C. Weng Deartment of Statistics
More informationScaling Multiple Point Statistics for Non-Stationary Geostatistical Modeling
Scaling Multile Point Statistics or Non-Stationary Geostatistical Modeling Julián M. Ortiz, Steven Lyster and Clayton V. Deutsch Centre or Comutational Geostatistics Deartment o Civil & Environmental Engineering
More informationEstimation of Relative Operating Characteristics of Text Independent Speaker Verification
International Journal of Engineering Science Invention Volume 1 Issue 1 December. 2012 PP.18-23 Estimation of Relative Operating Characteristics of Text Independent Speaker Verification Palivela Hema 1,
More informationCOURSE OUTLINE. Introduction Signals and Noise: 3) Analysis and Simulation Filtering Sensors and associated electronics. Sensors, Signals and Noise
Sensors, Signals and Noise 1 COURSE OUTLINE Introduction Signals and Noise: 3) Analysis and Simulation Filtering Sensors and associated electronics Noise Analysis and Simulation White Noise Band-Limited
More informationSystem Reliability Estimation and Confidence Regions from Subsystem and Full System Tests
009 American Control Conference Hyatt Regency Riverfront, St. Louis, MO, USA June 0-, 009 FrB4. System Reliability Estimation and Confidence Regions from Subsystem and Full System Tests James C. Sall Abstract
More informationEstimation of the large covariance matrix with two-step monotone missing data
Estimation of the large covariance matrix with two-ste monotone missing data Masashi Hyodo, Nobumichi Shutoh 2, Takashi Seo, and Tatjana Pavlenko 3 Deartment of Mathematical Information Science, Tokyo
More informationVIBRATION ANALYSIS OF BEAMS WITH MULTIPLE CONSTRAINED LAYER DAMPING PATCHES
Journal of Sound and Vibration (998) 22(5), 78 85 VIBRATION ANALYSIS OF BEAMS WITH MULTIPLE CONSTRAINED LAYER DAMPING PATCHES Acoustics and Dynamics Laboratory, Deartment of Mechanical Engineering, The
More informationOn Line Parameter Estimation of Electric Systems using the Bacterial Foraging Algorithm
On Line Parameter Estimation of Electric Systems using the Bacterial Foraging Algorithm Gabriel Noriega, José Restreo, Víctor Guzmán, Maribel Giménez and José Aller Universidad Simón Bolívar Valle de Sartenejas,
More informationPublished: 14 October 2013
Electronic Journal of Alied Statistical Analysis EJASA, Electron. J. A. Stat. Anal. htt://siba-ese.unisalento.it/index.h/ejasa/index e-issn: 27-5948 DOI: 1.1285/i275948v6n213 Estimation of Parameters of
More informationOne-way ANOVA Inference for one-way ANOVA
One-way ANOVA Inference for one-way ANOVA IPS Chater 12.1 2009 W.H. Freeman and Comany Objectives (IPS Chater 12.1) Inference for one-way ANOVA Comaring means The two-samle t statistic An overview of ANOVA
More informationA PEAK FACTOR FOR PREDICTING NON-GAUSSIAN PEAK RESULTANT RESPONSE OF WIND-EXCITED TALL BUILDINGS
The Seventh Asia-Pacific Conference on Wind Engineering, November 8-1, 009, Taiei, Taiwan A PEAK FACTOR FOR PREDICTING NON-GAUSSIAN PEAK RESULTANT RESPONSE OF WIND-EXCITED TALL BUILDINGS M.F. Huang 1,
More informationGIVEN an input sequence x 0,..., x n 1 and the
1 Running Max/Min Filters using 1 + o(1) Comarisons er Samle Hao Yuan, Member, IEEE, and Mikhail J. Atallah, Fellow, IEEE Abstract A running max (or min) filter asks for the maximum or (minimum) elements
More informationSAS for Bayesian Mediation Analysis
Paer 1569-2014 SAS for Bayesian Mediation Analysis Miočević Milica, Arizona State University; David P. MacKinnon, Arizona State University ABSTRACT Recent statistical mediation analysis research focuses
More informationSolved Problems. (a) (b) (c) Figure P4.1 Simple Classification Problems First we draw a line between each set of dark and light data points.
Solved Problems Solved Problems P Solve the three simle classification roblems shown in Figure P by drawing a decision boundary Find weight and bias values that result in single-neuron ercetrons with the
More informationGeneral Linear Model Introduction, Classes of Linear models and Estimation
Stat 740 General Linear Model Introduction, Classes of Linear models and Estimation An aim of scientific enquiry: To describe or to discover relationshis among events (variables) in the controlled (laboratory)
More informationA New Method of DDB Logical Structure Synthesis Using Distributed Tabu Search
A New Method of DDB Logical Structure Synthesis Using Distributed Tabu Search Eduard Babkin and Margarita Karunina 2, National Research University Higher School of Economics Det of nformation Systems and
More informationOne step ahead prediction using Fuzzy Boolean Neural Networks 1
One ste ahead rediction using Fuzzy Boolean eural etworks 1 José A. B. Tomé IESC-ID, IST Rua Alves Redol, 9 1000 Lisboa jose.tome@inesc-id.t João Paulo Carvalho IESC-ID, IST Rua Alves Redol, 9 1000 Lisboa
More informationADAPTIVE CONTROL METHODS FOR EXCITED SYSTEMS
ADAPTIVE CONTROL METHODS FOR NON-LINEAR SELF-EXCI EXCITED SYSTEMS by Michael A. Vaudrey Dissertation submitted to the Faculty of the Virginia Polytechnic Institute and State University in artial fulfillment
More informationEntropy Coding of Spectral Envelopes for Speech And Audio Coding Using Distribution Quantization
INTERSPEECH 2016 Setember 8 12, 2016, San Francisco, USA Entroy Coding of Sectral Enveloes for Seech And Audio Coding Using Distribution Quantization Srikanth Korse 1, Tobias Jähnel 2, Tom Bäckström 1,2
More informationChapter 1: PROBABILITY BASICS
Charles Boncelet, obability, Statistics, and Random Signals," Oxford University ess, 0. ISBN: 978-0-9-0005-0 Chater : PROBABILITY BASICS Sections. What Is obability?. Exeriments, Outcomes, and Events.
More informationDistributed K-means over Compressed Binary Data
1 Distributed K-means over Comressed Binary Data Elsa DUPRAZ Telecom Bretagne; UMR CNRS 6285 Lab-STICC, Brest, France arxiv:1701.03403v1 [cs.it] 12 Jan 2017 Abstract We consider a networ of binary-valued
More informationPARTIAL FACE RECOGNITION: A SPARSE REPRESENTATION-BASED APPROACH. Luoluo Liu, Trac D. Tran, and Sang Peter Chin
PARTIAL FACE RECOGNITION: A SPARSE REPRESENTATION-BASED APPROACH Luoluo Liu, Trac D. Tran, and Sang Peter Chin Det. of Electrical and Comuter Engineering, Johns Hokins Univ., Baltimore, MD 21218, USA {lliu69,trac,schin11}@jhu.edu
More informationMATHEMATICAL MODELLING OF THE WIRELESS COMMUNICATION NETWORK
Comuter Modelling and ew Technologies, 5, Vol.9, o., 3-39 Transort and Telecommunication Institute, Lomonosov, LV-9, Riga, Latvia MATHEMATICAL MODELLIG OF THE WIRELESS COMMUICATIO ETWORK M. KOPEETSK Deartment
More informationSinger Identification using MFCC and LPC and its comparison for ANN and Naïve Bayes Classifiers
Singer Identification using MFCC and LPC and its comparison for ANN and Naïve Bayes Classifiers Kumari Rambha Ranjan, Kartik Mahto, Dipti Kumari,S.S.Solanki Dept. of Electronics and Communication Birla
More informationConvolutional Codes. Lecture 13. Figure 93: Encoder for rate 1/2 constraint length 3 convolutional code.
Convolutional Codes Goals Lecture Be able to encode using a convolutional code Be able to decode a convolutional code received over a binary symmetric channel or an additive white Gaussian channel Convolutional
More informationYixi Shi. Jose Blanchet. IEOR Department Columbia University New York, NY 10027, USA. IEOR Department Columbia University New York, NY 10027, USA
Proceedings of the 2011 Winter Simulation Conference S. Jain, R. R. Creasey, J. Himmelsach, K. P. White, and M. Fu, eds. EFFICIENT RARE EVENT SIMULATION FOR HEAVY-TAILED SYSTEMS VIA CROSS ENTROPY Jose
More informationCalculation of MTTF values with Markov Models for Safety Instrumented Systems
7th WEA International Conference on APPLIE COMPUTE CIENCE, Venice, Italy, November -3, 7 3 Calculation of MTTF values with Markov Models for afety Instrumented ystems BÖCÖK J., UGLJEA E., MACHMU. University
More informationA PROBABILISTIC POWER ESTIMATION METHOD FOR COMBINATIONAL CIRCUITS UNDER REAL GATE DELAY MODEL
A PROBABILISTIC POWER ESTIMATION METHOD FOR COMBINATIONAL CIRCUITS UNDER REAL GATE DELAY MODEL G. Theodoridis, S. Theoharis, D. Soudris*, C. Goutis VLSI Design Lab, Det. of Electrical and Comuter Eng.
More informationL P -NORM NON-NEGATIVE MATRIX FACTORIZATION AND ITS APPLICATION TO SINGING VOICE ENHANCEMENT. Tomohiko Nakamura and Hirokazu Kameoka,
L P -NORM NON-NEGATIVE MATRIX FACTORIZATION AND ITS APPLICATION TO SINGING VOICE ENHANCEMENT Tomohio Naamura and Hiroazu Kameoa, Graduate School of Information Science and Technology, The University of
More informationIterative Methods for Designing Orthogonal and Biorthogonal Two-channel FIR Filter Banks with Regularities
R. Bregović and T. Saramäi, Iterative methods for designing orthogonal and biorthogonal two-channel FIR filter bans with regularities, Proc. Of Int. Worsho on Sectral Transforms and Logic Design for Future
More informationLinear diophantine equations for discrete tomography
Journal of X-Ray Science and Technology 10 001 59 66 59 IOS Press Linear diohantine euations for discrete tomograhy Yangbo Ye a,gewang b and Jiehua Zhu a a Deartment of Mathematics, The University of Iowa,
More information