Audio Signal Separation and Classification: A Review Paper
|
|
- Beverly Copeland
- 7 years ago
- Views:
Transcription
1 Audio Signa Separation and Cassification: A Review Paper Sik Smita 1, Sharmia Biswas 2, Sandeep Singh Soanki 3 Bira Institute of Technoogy, Mesra, Ranchi, India 1,2,3 ABSTRACT: Music signas are not soey characterized because of other mixed audio signas. Mixed audio signas contain music signas mixed with speech signas, voice and even background noise.thus, mixed signas need to cassify separatey. Researchers have deveoped many agorithms to sove this probem keeping in mind with their characteristic features of music signas: by timbre, harmony, pitch, oudness etc. The agorithm ICA (Independent component anaysis) uses basis of Bind source separation, HSS(Harmonic structure stabiity), "SOSM" APPROACH (Second Order Statistica Measures Approach), Sinusoida Parameters based audio cassification using FDMSM etc. are some of the mixed signa cassification agorithms. This paper highights a these existing methods and their experimenta resuts. KEYWORDS: Harmonic structure stabiity;ica; HSS; FDMSM; SOSM; audio separation; voice; music I. INTRODUCTION In this digita word, efficient management of the digita content of audio data has become an important area for the researcher. However, due to timeconsuming and expensive indexing and abeing that are performed manuay, an automatic contentbased cassification system of mixed type audio signa is a must for various appications of signas used these days. Separation of voice (speech), music and noise in a mixed audio signa is an important probem in music research. Here, voice means the singing voice in a song, music means the instrument. Separation of mixed signa is hepfu for many other music researches, such as Music Retrieva, Cassification and Segmentation, Mutipitch estimation, etc. Aong with audio signa separation, audio cassification is important due to foowing reasons: (a) Different audio types shoud be processed differenty. (b) The searching space after cassification is reduced into a particuar subcass during the retrieva process. This approach reduces the computationa compexity and one can choose specified fied of research for that particuar signa. For exampe, speech signas can be used for speech recognition, music signas for music anaysis and voice for speaker recognition. II. RELATED WORK In this review, music signas are of prime interest. Music signas are characterized according to their pitch, oudness, duration, harmonic structure and timbre. Timbre is mainy used for the recognition of sound sources. Music signas are more ordered than voice. The entropy of music is much constant in time than that of speech [1]. Among various methods [116] for signa separation, harmonic structure mode [4,5] has extended mutipitch estimation agorithm used to extract harmonic structures, and custering agorithm is used to cacuate harmonic structure modes. Then, signas are separated by using these modes to distinguish harmonic structures of different signas. ICA (Independent Component Anaysis) is one of the methods for extracting individua signas from mixtures. Its power resides in the physica assumptions that the different physica processes generate unreated signas. The simpe and generic nature of this assumption aows ICA to be successfuy appied in the diverse range of research fieds [5]. Vanroose used ICA to remove music background from speech by subtracting ICA components with the owest entropy [6]. Genera signa separation methods do not sufficienty utiize the specia character of music signas. GiJin and Te Copyright to IJIRCCE
2 Won proposed a probabiistic approach to singe channe bind signa separation [7], which is based on expoiting the inherent time structure of sound sources by earning a priori sets of basis fiters. In Harmonic Structure Stabiity approach [4], training sets are not required, and a information is directy earned from the mixed audio signa. Feng et a. appied FastICA to extract singing and accompaniment from a mixture [11]. Based on different features and characteristics, audio cassification is used to cassify most audio into speech, music and noise [23]. Many features are used incuding Cepstrum [4], [5], power spectrum or ZCR proposed in [6] using Support Vector Machine (SVM) cassifier. Sinusoida parameter based audio cassification method [5] introduce a new unsupervised Feature seection method; in order to determine which features are optimum is sense of high accuracy in cassification. Using ony these obtained features it is demonstrated that acceptabe cassification accuracy is achievabe. In addition, the SVM and Reevance Vector Machine (RVM) are empoyed since they were aready proven to be the best cassifiers for musica genre recognition in many iteratures. This cassification system categorizes audio based on new audio features, sinusoida parameters denoting harmonicity as we as Subband energy. These sinusoida parameters jointy represent harmonicity ratio (HR) and energies of different frequency subbands. Another method used for cassification is Statistica Discrimination and Identification of Some Acoustic Sounds [6].This method finds a preprocessing technique which aows discriminating between the different sounds. This technique is based on the simiarity measure μ Gc introduced by Bimbot and used for speaker recognition tasks. III. METHODOLOGY A. INDEPENDENT COMPONENT ANALYSIS[9] Bind Signa Separation (BSS) or Independent Component Anaysis (ICA) is the identification & separation of mixtures of sources with itte prior information. Nongaussianity is used to estimate the ICA mode. ICA invoves two basic steps (a) Noninear decorreation" In this step, matrix W is cacuated so that for any i j, the components y i and y j are uncorreated, and the transformed components g(y i ) and h(y j ) are uncorreated, where g and h are some suitabe noninear functions. (b) Maximum nongaussianity The oca maxima of nongaussianity of a inear combination y=wxunder the constraint that the variance of x is constant. Each oca maximum gives one independent component. B. HARMONIC STRUCTURE STABILITY ANALYSIS[4,5] This agorithm finds the average harmonic structure of the music, and then separate signas by using it to distinguish voice and music harmonic structures. Steps invoved in this agorithm are preprocessing, harmonic structure extraction, music Average Harmonic Structure anaysis, separation of signas. S(t) is a monophonic music signa. R s( t) A r( t) cos[ r( t)] e( t) r 1 A r( t) and r( t) t 2 o r f ( ) d 0 are the instantaneous ampitude and phase of the r th harmonic, respectivey, R is the maxima harmonic number, f 0 (τ) is the fundamentafrequency, e(t) is the nonharmonic or noise component. Divide s(t) into overapped frames and cacuate f 0 and A r by detecting peaks in the magnitude spectrum. A r =0, if there doesn t exist the r th harmonic. = 1,..., L is the frame index. f 0 and [A 1,...,A R ] describe the position and ampitudes of harmonics. Normaize A r by mutipying a factor ρ = C/A 1 ( C is an arbitrary constant) to eiminate the infuence of the ampitude. Transate the ampitudes into a og scae, because the human ear has a roughy ogarithmic sensitivity to signa intensity. Harmonic Structure Coefficient is then defined as an equation. The timbre of a sound is mosty controed by the number of harmonics and the ratio of their ampitudes, so B = [B 1,...,B R], which is free from the fundamenta frequency and ampitude, exacty represents the timbre of a sound. In this paper, these coefficients are used to represent the harmonic structure of a sound. Average Harmonic Structure and Harmonic Copyright to IJIRCCE
3 Structure Stabiity are defined as foows to mode music signas and measure the stabiity of harmonic structures. Harmonic Structure B, B i is Harmonic Structure Coefficient: B = [B 1,...,B R], B i=og ( ρ A i )/og ( ρ A 1 ),i=1,,r Average Harmonic Structure (AHS): L 1 B B L 1 Harmonic Structure Stabiity (HSS): L 2 R L H S S B B ( B r B r ) R L 1 R L r 1 1 AHS and HSS are the mean and variance of B. Since timbres of most instruments are stabe, B varies itte in different frames in a music signa and AHS is a good mode to represent music signas. On the contrary B, B varies much in a voice signa and the corresponding HSS is much bigger than that of the music signa. C. Second Order Statistica Measures Approach[7] This method [5], based on monogaussian mode [3], uses some measures of simiarity, which are caed Second Order Statistica Measures (SOSM). These measures are used in order to recognize the speaker at each segment of the speech signa. Two measures (μ GC0.5 and μ G0.5 ) are used inthis experiment. In the case of sounds cassification, measuring the simiarity rate by the μ Gc0.5 permits to have an idea on the type of considered sound. If μ Gc 0.5 [Threshod_minj, Threshod _maxj] then we identify the considered sound as type «j».where, Threshod_min j = min(μgc 0.5 ( speech,sound f )) Threshod_max j = max(μgc 0.5 ( speech,sound j )) Where, the word «sound» represents a set of sounds with the same type. And «speech» represents a set of reference speakers. J denotes a certain sound beonging to a certain cass j. D. Sinusoida Parameters based audio cassification [8] Speech signa contains both periodic and nonperiodic information due to the impusive nature of events or noiseike processes occurring in unvoiced regions. As a resut, we can write for a time window segment of the underying observed audio signa as foows: L a c o s (2 f n + ) + (n ) = 1 y(n) =,, where n = 1,,N s is the sampe index, θ= ( a f ) denote the sinusoida parameters incuding the ampitude, frequency, and phase of the th sinusoida component, respectivey, L is the number of sinusoida components in the signa, and (n) is the observation noise modeed as a zeromean, additive Gaussian noise sequence. In genera, it is of interest to estimate the corresponding sinusoida parameters incuding frequencies f, ampitudes a, phases and the number of sinusoids, L. Figure 1[8] shows the basic bock diagram of the process which is FDMSM mode. Whereas, Figure 2 shows the bock diagram of the proposed audio cassification system [8]. Figure 1 For extracting the sinusoida parameters incuding ampitudes and their reated frequencies in some Mebands, modified version ofthestateoftheart sinusoida mode aso caed Fixed Dimension Modified Sinusoida Mode (FDMSM) is empoyed [16]. At the end of the sinusoida anaysis using the FDMSM approach we reach at tripe features for each time window frame as: Copyright to IJIRCCE
4 φ= ( a, f, ) Figure 2 Further on Feature seection is done to find the smaest subset of the origina features using an unsupervised method as preprocess. IV. DISCUSSIONS ON RESULTS OBTAINED A. INDEPENDENT COMPONENT ANALYSIS [9] ICAexperimenta resuts are shown in Figure 3[9].The observed mixture of signas is shown in Figure 4[9].The origina speech signas are presented in Figure 3 and the mixed signas is shown in Figure4. The ICA agorithm is used to recover the data in Figure3 using ony the data in Figure4. Figure 3 Figure 4 B. HARMONIC STRUCTURE STABILITY ANALYSIS [4,5] In Figure 5[4,5], it can be seenthat the mixtures are we separated. The distance between music harmonic structures and the corresponding music AHS is sma (the mean distances is 0.01 and in experiment 1 and 2, respectivey), and the distance between voice harmonic structures and the music AHS is bigger (the mean distances is 0.1 and 0.13 in experiment 1 and 2, respectivey). So, the music AHS is a good mode for music signa representation and for voice and music separation. Copyright to IJIRCCE
5 Fig. 5 aso shows speech enhancement resuts obtained by speech enhancement software which tries to estimate the spectrum of noise in the pause of speech and enhance the speech by spectra subtraction. Figure 5 C. Second Order Statistica Measures Approach[7] This method has two aims: firsty, the discrimination between speech and other sounds; secondy the cassification of the different acoustica sounds according to the μ Gc vaues, used as a measure of simiarity, corresponding for each sound. For exampe, if μ Gc is within [ ] then we can state that given sound shoud be music (Tabe 1).Tabe 1[7] shows Cassification of sounds according to the μ Gc range. Tabe 1 μ Gc range Probabe cassification Speech of a known speaker Speech of a different speaker Speech of a different speaker or human noise Office noise Music Background noise D. Sinusoida Parameters based audio cassification[8] In this approach, assume that S(k) be the spectrum of current speech frame. To accompish the peak picking process 8 msec hop size is chosen. It has fixed number of sinusoida parameters and aso preserve the synthesize quaity as cose as possibe to the origina signa in terms of perception. This resuts in the significant reduction as we as better custering performance. Tabe 2[8] shows mixed type audio cassification accuracy. Tabe 2 Cassifier Accuracy Parameters Kerne MLP 91.3 SVM KNN input 6 hidden ayer neurons Linear γ=0.5 γ =0.3 Max number of neighbors= 3 Max number of neighbors= 2 RBF RBF RBF RVM γ = 3 RBF As it was enightened in simuation resuts, empoying such sinusoida parameters aong with the socaed SVM Copyright to IJIRCCE
6 cassifier, wi successfuy improve the performance of the proposed cassification. Figure 6[8] is showing the decision boundaries using SVM[8]. Figure 6 V. CONCLUSION Signa separation is a difficut probem and no reiabe methods are avaiabe for the genera case. The detaied research on a the works done on separation of audio signas has ed us to the concusion that Harmonic Structure characteristic is the effective with respect to others as it preserves the audio quaity. Moreover, most of the instrument sounds are harmonic in nature. Harmonic structure of music signa is stabe. Harmonic structures of the music signas performed by different instruments are different. So, we can easiy cassify and separate different signas. However, this agorithm doesn t work for nonharmonic instruments, such as some drums. Some rhythm tracking agorithms can be used instead to separate drum sounds. Preprocessing techniques ike sinusoida parameter approach and SOSM approach have aso ed us to the concusion that seection of feature is an important step in the research fied especiay in music. That is why, the need to seect the most efficient and accurate feature is essentia for audio cassification. Cassifiers ike SVM are proven to be the best cassifiers as they more accurate than other athough it may get trade off by the eapsed time. REFERENCES 1. J. Pinquier, J. Rouas, and R. A. Obrecht, Robust speech / music cassification in audio documents, Internationa ConferenceOn Spoken Language Processing (ICSLP), pp , G. R. Naik and D. K. Kumar An Overview of Independent Component Anaysis and Its Appications, Informatica 35,pp , P. Vanroose, Bind source separation of speech and background music for improved speech recognition, The 24th Symposium on Information Theory, pp , May Y. G. Zhang and C. S. Zhang Separation of voice and music by Harmonic Structure Stabity Anaysis, Mutimedia and Expo, ICME 2005, pp , Y. G. Zhang and C. S. Zhang Separation of Music Signas by Harmonic Structure Modeing,NIPS, P. M. B. Mahae, M. Rashidi, K. Faez, A. Sayadiyan A New SVMbased Mix Audio Cassification, NIPS, H. Sayoud, S. Ouamour Statistica Discrimination and Identification of Some Acoustic Sounds, GCC Conference (GCC),pp.1 5, P. MowaeeBegzadeMahae, A. Sayadiyan, and K. Faez Mixed Type Audio Cassification using Sinusoida Parameters, 3rd Internationa Conference on Information and Communication Technoogies (ICTTA), A. Hyvärinen and E. Oja Independent Component Anaysis: Agorithms and Appications, Vo 13, pp, , A. Ozerov, P. Phiippe, F. Bimbot, and R. Gribonva Adaptation of Bayesian Modes for SingeChanne Source Separation and its Appicationto Voice/Music Separation in Popuar Songs,IEEE Transactions on Audio, Speech, and Language Processing, Vo.15, Issue: 5,pp , S. Kova, M. Stobov, and M. Khitrov, Broadband noise canceation systems: new approach to working performance optimization, in EUROSPEECH 99, pp , G. J. Jang and T. W. Lee, A probabiistic approachto singe channe bind signa separation, NIPS, Y. Feng, Y. Zhuang, and Y. Pan, Popuar music retrieva by independent component anaysis, in ISMIR, pp , Anssikapuri and Manue Davy Signa Processing methods for transcription, springer science, pp. 69, F. BIMBOT, I. MAGRINCHAGNOLLEAU, et L. MATHAN 1995, SecondOrder Statistica measures for textindependent Broadcaster Identification. Speech Communication, Voume. 17, Number, 12, pp , August S. Z. Li, Contentbased audio cassification and retrieva using the nearest feature ine method, IEEE Transaction on Speech and Audio Processing, Vo. 8, Issue: 5, pp Copyright to IJIRCCE
7 BIOGRAPHY Sik Smitais a Masters in Engineering student in the Eectronics Department, Bira Institute of Technoogy, Deemed University. Her research interests are music signa processing and signa processing. SharmiaBiswasis PhD student in the Eectronics Department, Bira Institute of Technoogy, Deemed University. Her research interests are music signa processing and signa processing. Sandeep Singh Soankiis Associate Professor in the Eectronics Department, Bira Institute of Technoogy, Deemed University. His research interests are music and speech signa processing and automation. Copyright to IJIRCCE
ONE of the most challenging problems addressed by the
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, VOL. 44, NO. 9, SEPTEMBER 2006 2587 A Mutieve Context-Based System for Cassification of Very High Spatia Resoution Images Lorenzo Bruzzone, Senior Member,
More informationA Similarity Search Scheme over Encrypted Cloud Images based on Secure Transformation
A Simiarity Search Scheme over Encrypted Coud Images based on Secure Transormation Zhihua Xia, Yi Zhu, Xingming Sun, and Jin Wang Jiangsu Engineering Center o Network Monitoring, Nanjing University o Inormation
More informationFace Hallucination and Recognition
Face Haucination and Recognition Xiaogang Wang and Xiaoou Tang Department of Information Engineering, The Chinese University of Hong Kong {xgwang1, xtang}@ie.cuhk.edu.hk http://mmab.ie.cuhk.edu.hk Abstract.
More informationSecure Network Coding with a Cost Criterion
Secure Network Coding with a Cost Criterion Jianong Tan, Murie Médard Laboratory for Information and Decision Systems Massachusetts Institute of Technoogy Cambridge, MA 0239, USA E-mai: {jianong, medard}@mit.edu
More informationAn Idiot s guide to Support vector machines (SVMs)
An Idiot s guide to Support vector machines (SVMs) R. Berwick, Viage Idiot SVMs: A New Generation of Learning Agorithms Pre 1980: Amost a earning methods earned inear decision surfaces. Linear earning
More informationAustralian Bureau of Statistics Management of Business Providers
Purpose Austraian Bureau of Statistics Management of Business Providers 1 The principa objective of the Austraian Bureau of Statistics (ABS) in respect of business providers is to impose the owest oad
More informationSimultaneous Routing and Power Allocation in CDMA Wireless Data Networks
Simutaneous Routing and Power Aocation in CDMA Wireess Data Networks Mikae Johansson *,LinXiao and Stephen Boyd * Department of Signas, Sensors and Systems Roya Institute of Technoogy, SE 00 Stockhom,
More informationCOMPARISON OF DIFFUSION MODELS IN ASTRONOMICAL OBJECT LOCALIZATION
COMPARISON OF DIFFUSION MODELS IN ASTRONOMICAL OBJECT LOCALIZATION Františe Mojžíš Department of Computing and Contro Engineering, ICT Prague, Technicá, 8 Prague frantise.mojzis@vscht.cz Abstract This
More informationFRAME BASED TEXTURE CLASSIFICATION BY CONSIDERING VARIOUS SPATIAL NEIGHBORHOODS. Karl Skretting and John Håkon Husøy
FRAME BASED TEXTURE CLASSIFICATION BY CONSIDERING VARIOUS SPATIAL NEIGHBORHOODS Kar Skretting and John Håkon Husøy University of Stavanger, Department of Eectrica and Computer Engineering N-4036 Stavanger,
More informationMulti-Robot Task Scheduling
Proc of IEEE Internationa Conference on Robotics and Automation, Karsruhe, Germany, 013 Muti-Robot Tas Scheduing Yu Zhang and Lynne E Parer Abstract The scheduing probem has been studied extensivey in
More informationVendor Performance Measurement Using Fuzzy Logic Controller
The Journa of Mathematics and Computer Science Avaiabe onine at http://www.tjmcs.com The Journa of Mathematics and Computer Science Vo.2 No.2 (2011) 311-318 Performance Measurement Using Fuzzy Logic Controer
More informationSABRe B2.1: Design & Development. Supplier Briefing Pack.
SABRe B2.1: Design & Deveopment. Suppier Briefing Pack. 2013 Ros-Royce pc The information in this document is the property of Ros-Royce pc and may not be copied or communicated to a third party, or used
More informationA Supplier Evaluation System for Automotive Industry According To Iso/Ts 16949 Requirements
A Suppier Evauation System for Automotive Industry According To Iso/Ts 16949 Requirements DILEK PINAR ÖZTOP 1, ASLI AKSOY 2,*, NURSEL ÖZTÜRK 2 1 HONDA TR Purchasing Department, 41480, Çayırova - Gebze,
More informationA Latent Variable Pairwise Classification Model of a Clustering Ensemble
A atent Variabe Pairwise Cassification Mode of a Custering Ensembe Vadimir Berikov Soboev Institute of mathematics, Novosibirsk State University, Russia berikov@math.nsc.ru http://www.math.nsc.ru Abstract.
More informationElectronic Commerce Research and Applications
Eectronic Commerce Research and Appications 8 (2009) 63 77 Contents ists avaiabe at ScienceDirect Eectronic Commerce Research and Appications journa homepage: www.esevier.com/ocate/ecra Forecasting market
More informationCONTRIBUTION OF INTERNAL AUDITING IN THE VALUE OF A NURSING UNIT WITHIN THREE YEARS
Dehi Business Review X Vo. 4, No. 2, Juy - December 2003 CONTRIBUTION OF INTERNAL AUDITING IN THE VALUE OF A NURSING UNIT WITHIN THREE YEARS John N.. Var arvatsouakis atsouakis DURING the present time,
More informationUS 20060288075Al (19) United States (12) Patent Application Publication (10) Pub. No.: US 2006/0288075 A1 Wu (57) A sender is selectively input- S301
US 20060288075A (19) United States (12) Patent Appication Pubication (10) Pub. No.: US 2006/0288075 A1 Wu (43) Pub. Date: Dec. 21, 2006 (54) ELECTRONIC MAILBOX ADDRESS BOOK MANAGEMENT SYSTEM AND METHOD
More informationChapter 3: e-business Integration Patterns
Chapter 3: e-business Integration Patterns Page 1 of 9 Chapter 3: e-business Integration Patterns "Consistency is the ast refuge of the unimaginative." Oscar Wide In This Chapter What Are Integration Patterns?
More informationBLIND SOURCE SEPARATION OF SPEECH AND BACKGROUND MUSIC FOR IMPROVED SPEECH RECOGNITION
BLIND SOURCE SEPARATION OF SPEECH AND BACKGROUND MUSIC FOR IMPROVED SPEECH RECOGNITION P. Vanroose Katholieke Universiteit Leuven, div. ESAT/PSI Kasteelpark Arenberg 10, B 3001 Heverlee, Belgium Peter.Vanroose@esat.kuleuven.ac.be
More information3.3 SOFTWARE RISK MANAGEMENT (SRM)
93 3.3 SOFTWARE RISK MANAGEMENT (SRM) Fig. 3.2 SRM is a process buit in five steps. The steps are: Identify Anayse Pan Track Resove The process is continuous in nature and handed dynamicay throughout ifecyce
More informationTelephony Trainers with Discovery Software
Teephony Trainers 58 Series Teephony Trainers with Discovery Software 58-001 Teephony Training System 58-002 Digita Switching System 58-003 Digita Teephony Training System 58-004 Digita Trunk Network System
More informationTeamwork. Abstract. 2.1 Overview
2 Teamwork Abstract This chapter presents one of the basic eements of software projects teamwork. It addresses how to buid teams in a way that promotes team members accountabiity and responsibiity, and
More informationVirtual trunk simulation
Virtua trunk simuation Samui Aato * Laboratory of Teecommunications Technoogy Hesinki University of Technoogy Sivia Giordano Laboratoire de Reseaux de Communication Ecoe Poytechnique Federae de Lausanne
More informationTraffic classification-based spam filter
Traffic cassification-based spam fiter Ni Zhang 1,2, Yu Jiang 3, Binxing Fang 1, Xueqi Cheng 1, Li Guo 1 1 Software Division, Institute of Computing Technoogy, Chinese Academy of Sciences, 100080, Beijing,
More informationDynamic Pricing Trade Market for Shared Resources in IIU Federated Cloud
Dynamic Pricing Trade Market or Shared Resources in IIU Federated Coud Tongrang Fan 1, Jian Liu 1, Feng Gao 1 1Schoo o Inormation Science and Technoogy, Shiiazhuang Tiedao University, Shiiazhuang, 543,
More informationA New Statistical Approach to Network Anomaly Detection
A New Statistica Approach to Network Anomay Detection Christian Caegari, Sandrine Vaton 2, and Michee Pagano Dept of Information Engineering, University of Pisa, ITALY E-mai: {christiancaegari,mpagano}@ietunipiit
More informationUniversity of Southern California
Master of Science in Financia Engineering Viterbi Schoo of Engineering University of Southern Caifornia Dia 1-866-469-3239 (Meeting number 924 898 113) to hear the audio portion, or isten through your
More informationNetwork/Communicational Vulnerability
Automated teer machines (ATMs) are a part of most of our ives. The major appea of these machines is convenience The ATM environment is changing and that change has serious ramifications for the security
More informationSpherical Correlation of Visual Representations for 3D Model Retrieval
Noname manuscript No. (wi be inserted by the editor) Spherica Correation of Visua Representations for 3D Mode Retrieva Ameesh Makadia Kostas Daniiidis the date of receipt and acceptance shoud be inserted
More informationLeakage detection in water pipe networks using a Bayesian probabilistic framework
Probabiistic Engineering Mechanics 18 (2003) 315 327 www.esevier.com/ocate/probengmech Leakage detection in water pipe networks using a Bayesian probabiistic framework Z. Pouakis, D. Vaougeorgis, C. Papadimitriou*
More informationABSTRACT. Categories and Subject Descriptors. General Terms. Keywords 1. INTRODUCTION. Jun Yin, Ye Wang and David Hsu
Jun Yin, Ye Wang and David Hsu ABSTRACT Prompt feedback is essentia for beginning vioin earners; however, most amateur earners can ony meet with teachers and receive feedback once or twice a week. To hep
More informationA 3D Motion Trajectory Signature Identification for Human- Computer Interaction (HCI) Applications
Internationa Journa of esearch Studies in Science, Engineering and Technoogy Voume 2, Issue 7, Juy 2015, PP 102-110 ISSN 2349-4751 (Print) & ISSN 2349-476X (Onine) A 3D Motion Trajectory Signature Identification
More informationLearning from evaluations Processes and instruments used by GIZ as a learning organisation and their contribution to interorganisational learning
Monitoring and Evauation Unit Learning from evauations Processes and instruments used by GIZ as a earning organisation and their contribution to interorganisationa earning Contents 1.3Learning from evauations
More informationAn Integrated Data Management Framework of Wireless Sensor Network
An Integrated Data Management Framework of Wireess Sensor Network for Agricutura Appications 1,2 Zhao Liang, 2 He Liyuan, 1 Zheng Fang, 1 Jin Xing 1 Coege of Science, Huazhong Agricutura University, Wuhan
More informationFigure 1. A Simple Centrifugal Speed Governor.
ENGINE SPEED CONTROL Peter Westead and Mark Readman, contro systems principes.co.uk ABSTRACT: This is one of a series of white papers on systems modeing, anaysis and contro, prepared by Contro Systems
More informationFast Robust Hashing. ) [7] will be re-mapped (and therefore discarded), due to the load-balancing property of hashing.
Fast Robust Hashing Manue Urueña, David Larrabeiti and Pabo Serrano Universidad Caros III de Madrid E-89 Leganés (Madrid), Spain Emai: {muruenya,darra,pabo}@it.uc3m.es Abstract As statefu fow-aware services
More informationCreat-Poreen Power Electronics Co., Ltd
(STOCK CODE) 002350 Creat-Poreen Power Eectronics Co., Ltd Address: 4F, Xue Zhi Xuan Mansion, NO.16 Xue Qing Road, Hasidian District, Beijing, 100083 Te: +86 (010) 82755151 Fax: +86 (010) 82755268 Website:
More informationACO and SVM Selection Feature Weighting of Network Intrusion Detection Method
, pp. 129-270 http://dx.doi.org/10.14257/ijsia.2015.9.4.24 ACO and SVM Seection Feature Weighting of Network Intrusion Detection Method Wang Xingzhu Furong Coege Hunan, University of Arts and Science,
More informationREADING A CREDIT REPORT
Name Date CHAPTER 6 STUDENT ACTIVITY SHEET READING A CREDIT REPORT Review the sampe credit report. Then search for a sampe credit report onine, print it off, and answer the questions beow. This activity
More informationRestoration of blue scratches in digital image sequences
Avaiabe onine at www.sciencedirect.com Image and Vision Computing 26 (2008) 1314 1326 www.esevier.com/ocate/imavis Restoration of bue scratches in digita image sequences Lucia Maddaena a, *, Afredo Petrosino
More informationGREEN: An Active Queue Management Algorithm for a Self Managed Internet
: An Active Queue Management Agorithm for a Sef Managed Internet Bartek Wydrowski and Moshe Zukerman ARC Specia Research Centre for Utra-Broadband Information Networks, EEE Department, The University of
More informationFixed income managers: evolution or revolution
Fixed income managers: evoution or revoution Traditiona approaches to managing fixed interest funds rey on benchmarks that may not represent optima risk and return outcomes. New techniques based on separate
More informationLecture 7 Datalink Ethernet, Home. Datalink Layer Architectures
Lecture 7 Dataink Ethernet, Home Peter Steenkiste Schoo of Computer Science Department of Eectrica and Computer Engineering Carnegie Meon University 15-441 Networking, Spring 2004 http://www.cs.cmu.edu/~prs/15-441
More informationSentiment Analysis with Global Topics and Local Dependency
Proceedings of the Tenty-Fourth AAAI Conference on Artificia Inteigence (AAAI-10) Sentiment Anaysis ith Goba Topics and Loca Dependency Fangtao Li, Minie Huang, Xiaoyan Zhu State Key Laboratory of Inteigent
More informationPresented at the 107th Convention 1999 September 24-27 New York
Room Simuation for Mutichanne Fim and Music 4993 (B-2) Knud Bank Christensen and Thomas Lund TC Eectronic A/S DK-8240 Risskov, Denmark Presented at the 107th Convention 1999 September 24-27 New York This
More informationIEICE TRANS. INF. & SYST., VOL.E200 D, NO.1 JANUARY 2117 1
IEICE TRANS. INF. & SYST., VOL.E200 D, NO. JANUARY 27 PAPER Specia Issue on Information Processing Technoogy for web utiization A new simiarity measure to understand visitor behavior in a web site Juan
More informationEFFICIENT CLUSTERING OF VERY LARGE DOCUMENT COLLECTIONS
Chapter 1 EFFICIENT CLUSTERING OF VERY LARGE DOCUMENT COLLECTIONS Inderjit S. Dhion, James Fan and Yuqiang Guan Abstract An invauabe portion of scientific data occurs naturay in text form. Given a arge
More informationSwingtum A Computational Theory of Fractal Dynamic Swings and Physical Cycles of Stock Market in A Quantum Price-Time Space
Swingtum A Computationa Theory of Fracta Dynamic Swings and Physica Cyces of Stock Market in A Quantum Price-Time Space Heping Pan Schoo of Information Technoogy and Mathematica Sciences, University of
More informationEnabling Direct Interest-Aware Audience Selection
Enabing Direct Interest-Aware Audience Seection ABSTRACT Arie Fuxman Microsoft Research Mountain View, CA arief@microsoft.com Zhenhui Li University of Iinois Urbana-Champaign, Iinois zi28@uiuc.edu Advertisers
More informationSELECTING THE SUITABLE ERP SYSTEM: A FUZZY AHP APPROACH. Ufuk Cebeci
SELECTING THE SUITABLE ERP SYSTEM: A FUZZY AHP APPROACH Ufuk Cebeci Department of Industria Engineering, Istanbu Technica University, Macka, Istanbu, Turkey - ufuk_cebeci@yahoo.com Abstract An Enterprise
More informationArt of Java Web Development By Neal Ford 624 pages US$44.95 Manning Publications, 2004 ISBN: 1-932394-06-0
IEEE DISTRIBUTED SYSTEMS ONLINE 1541-4922 2005 Pubished by the IEEE Computer Society Vo. 6, No. 5; May 2005 Editor: Marcin Paprzycki, http://www.cs.okstate.edu/%7emarcin/ Book Reviews: Java Toos and Frameworks
More information3.5 Pendulum period. 2009-02-10 19:40:05 UTC / rev 4d4a39156f1e. g = 4π2 l T 2. g = 4π2 x1 m 4 s 2 = π 2 m s 2. 3.5 Pendulum period 68
68 68 3.5 Penduum period 68 3.5 Penduum period Is it coincidence that g, in units of meters per second squared, is 9.8, very cose to 2 9.87? Their proximity suggests a connection. Indeed, they are connected
More informationPricing Internet Services With Multiple Providers
Pricing Internet Services With Mutipe Providers Linhai He and Jean Warand Dept. of Eectrica Engineering and Computer Science University of Caifornia at Berkeey Berkeey, CA 94709 inhai, wr@eecs.berkeey.edu
More informationHow To Restore A Bue Scratch In Digita Image Sequences
Consigio Nazionae dee Ricerche Istituto di Cacoo e Reti ad Ate Prestazioni Restoration of bue scratches in digita image sequences Lucia Maddaena, Afredo Petrosino RT-ICAR-NA-05-2 December 2005 Consigio
More informationAssignment of Multiplicative Mixtures in Natural Images
Assignment of Mutipicative Mixtures in Natura Images Odeia Schwartz HHMI and Sak Institute La Joa, CA 94 odeia@sak.edu Terrence J. Sejnowski HHMI and Sak Institute La Joa, CA 94 terry@sak.edu Abstract
More informationMeasuring operational risk in financial institutions
Measuring operationa risk in financia institutions Operationa risk is now seen as a major risk for financia institutions. This paper considers the various methods avaiabe to measure operationa risk, and
More informationMaintenance activities planning and grouping for complex structure systems
Maintenance activities panning and grouping for compex structure systems Hai Canh u, Phuc Do an, Anne Barros, Christophe Berenguer To cite this version: Hai Canh u, Phuc Do an, Anne Barros, Christophe
More informationNo. of Pages 15, Model 5G ARTICLE IN PRESS. Contents lists available at ScienceDirect. Electronic Commerce Research and Applications
Eectronic Commerce Research and Appications xxx (2008) xxx xxx 1 Contents ists avaiabe at ScienceDirect Eectronic Commerce Research and Appications journa homepage: www.esevier.com/ocate/ecra 2 Forecasting
More informationChapter 6 Signal Data Mining from Wearable Systems
Chapter 6 Signa Data Mining from Wearabe Systems Francois G. Meyer 6.1 Definition of the Subject 6.1.1 Introduction Sensors from wearabe systems can be anayzed in rea-time on-site, or can be transmitted
More informationAdvanced ColdFusion 4.0 Application Development - 3 - Server Clustering Using Bright Tiger
Advanced CodFusion 4.0 Appication Deveopment - CH 3 - Server Custering Using Bri.. Page 1 of 7 [Figures are not incuded in this sampe chapter] Advanced CodFusion 4.0 Appication Deveopment - 3 - Server
More informationNordic Ecolabelling of Copy and printing paper - supplementary module
rdic Ecoabeing of Copy and printing paper - suppementary modue Version 4.1 22 June 2011 30 June 2016 rdic Ecoabeing Content What is rdic Ecoabeed copy and printing paper? 3 Why choose the rdic Ecoabe?
More informationChapter 3: JavaScript in Action Page 1 of 10. How to practice reading and writing JavaScript on a Web page
Chapter 3: JavaScript in Action Page 1 of 10 Chapter 3: JavaScript in Action In this chapter, you get your first opportunity to write JavaScript! This chapter introduces you to JavaScript propery. In addition,
More informationPrecise assessment of partial discharge in underground MV/HV power cables and terminations
QCM-C-PD-Survey Service Partia discharge monitoring for underground power cabes Precise assessment of partia discharge in underground MV/HV power cabes and terminations Highy accurate periodic PD survey
More informationOracle Hyperion Tax Provision. User's Guide Release 11.1.2.2
Orace Hyperion Tax Provision User's Guide Reease 11.1.2.2 Tax Provision User's Guide, 11.1.2.2 Copyright 2013, Orace and/or its affiiates. A rights reserved. Authors: EPM Information Deveopment Team Orace
More informationFast Image Acquisition in Pulse-Echo Ultrasound Imaging Using Compressed Sensing
Fast Image Acquisition in Puse-Echo Utrasound Imaging Using Compressed Sensing Martin F. Schiffner and Georg Schmitz Chair of Medica Engineering, Ruhr-Universität Bochum, D-4481 Bochum, Germany Copyright
More informationInsertion and deletion correcting DNA barcodes based on watermarks
Kracht and Schober BMC Bioinformatics (2015) 16:50 DOI 10.1186/s12859-015-0482-7 METHODOLOGY ARTICLE Open Access Insertion and deetion correcting DNA barcodes based on watermarks David Kracht * and Steffen
More informationOracle Project Financial Planning. User's Guide Release 11.1.2.2
Orace Project Financia Panning User's Guide Reease 11.1.2.2 Project Financia Panning User's Guide, 11.1.2.2 Copyright 2012, Orace and/or its affiiates. A rights reserved. Authors: EPM Information Deveopment
More informationNon-orthogonal Direct Access for Small Data Transmission in Cellular MTC Networks
Non-orthogona Direct Access for Sma Data Transmission in Ceuar MTC Networks Keng-Te Liao, Chia-Han Lee, Tzu-Ming Lin, Chien-Min Lee, and Wen-Tsuen Chen Academia Sinica, Taipei, Taiwan Industria Technoogy
More informationPacket Classification with Network Traffic Statistics
Packet Cassification with Network Traffic Statistics Yaxuan Qi and Jun Li Research Institute of Information Technoogy (RIIT), Tsinghua Uniersity Beijing, China, 100084 Abstract-- Packet cassification on
More informationEnhanced continuous, real-time detection, alarming and analysis of partial discharge events
DMS PDMG-RH DMS PDMG-RH Partia discharge monitor for GIS Partia discharge monitor for GIS Enhanced continuous, rea-time detection, aarming and anaysis of partia discharge events Unrivaed PDM feature set
More informationUnderstanding. nystagmus. RCOphth
Understanding nystagmus RCOphth RNIB s understanding series The understanding series is designed to hep you, your friends and famiy understand a itte bit more about your eye condition. Other tites in the
More informationNCH Software Bolt PDF Printer
NCH Software Bot PDF Printer This user guide has been created for use with Bot PDF Printer Version 1.xx NCH Software Technica Support If you have difficuties using Bot PDF Printer pease read the appicabe
More informationLet s get usable! Usability studies for indexes. Susan C. Olason. Study plan
Let s get usabe! Usabiity studies for indexes Susan C. Oason The artice discusses a series of usabiity studies on indexes from a systems engineering and human factors perspective. The purpose of these
More informationCertificate in Contemporary Music 2016 For International Applicants
Certificate in Contemporary Music 2016 For Internationa Appicants Quaification Certificate in Contemporary Music Performance Programme eve: Leve 4 Length: Start dates: Study options: One year 15 February
More informationBreakeven analysis and short-term decision making
Chapter 20 Breakeven anaysis and short-term decision making REAL WORLD CASE This case study shows a typica situation in which management accounting can be hepfu. Read the case study now but ony attempt
More informationChapter 1 Structural Mechanics
Chapter Structura echanics Introduction There are many different types of structures a around us. Each structure has a specific purpose or function. Some structures are simpe, whie others are compex; however
More informationThe Use of Cooling-Factor Curves for Coordinating Fuses and Reclosers
he Use of ooing-factor urves for oordinating Fuses and Recosers arey J. ook Senior Member, IEEE S& Eectric ompany hicago, Iinois bstract his paper describes how to precisey coordinate distribution feeder
More informationHuman Capital & Human Resources Certificate Programs
MANAGEMENT CONCEPTS Human Capita & Human Resources Certificate Programs Programs to deveop functiona and strategic skis in: Human Capita // Human Resources ENROLL TODAY! Contract Hoder Contract GS-02F-0010J
More informationIterative Water-filling for Load-balancing in Wireless LAN or Microcellular Networks
terative Water-fiing for Load-baancing in Wireess LAN or Microceuar Networks Jeremy K. Chen Theodore S. Rappaport Gustavo de Veciana Wireess Networking and Communications Group (WNCG), be University of
More informationIntroduction to XSL. Max Froumentin - W3C
Introduction to XSL Max Froumentin - W3C Introduction to XSL XML Documents Stying XML Documents XSL Exampe I: Hamet Exampe II: Mixed Writing Modes Exampe III: database Other Exampes How do they do that?
More informationIntegrating Risk into your Plant Lifecycle A next generation software architecture for risk based
Integrating Risk into your Pant Lifecyce A next generation software architecture for risk based operations Dr Nic Cavanagh 1, Dr Jeremy Linn 2 and Coin Hickey 3 1 Head of Safeti Product Management, DNV
More informationNormalization of Database Tables. Functional Dependency. Examples of Functional Dependencies: So Now what is Normalization? Transitive Dependencies
ISM 602 Dr. Hamid Nemati Objectives The idea Dependencies Attributes and Design Understand concepts normaization (Higher-Leve Norma Forms) Learn how to normaize tabes Understand normaization and database
More informationPREFACE. Comptroller General of the United States. Page i
- I PREFACE T he (+nera Accounting Office (GAO) has ong beieved that the federa government urgenty needs to improve the financia information on which it bases many important decisions. To run our compex
More informationTechnical Support Guide for online instrumental lessons
Technica Support Guide for onine instrumenta essons This is a technica guide for Music Education Hubs, Schoos and other organisations participating in onine music essons. The guidance is based on the technica
More informationDesign of Follow-Up Experiments for Improving Model Discrimination and Parameter Estimation
Design of Foow-Up Experiments for Improving Mode Discrimination and Parameter Estimation Szu Hui Ng 1 Stephen E. Chick 2 Nationa University of Singapore, 10 Kent Ridge Crescent, Singapore 119260. Technoogy
More informationLADDER SAFETY Table of Contents
Tabe of Contents SECTION 1. TRAINING PROGRAM INTRODUCTION..................3 Training Objectives...........................................3 Rationae for Training.........................................3
More informationPENALTY TAXES ON CORPORATE ACCUMULATIONS
H Chapter Six H PENALTY TAXES ON CORPORATE ACCUMULATIONS INTRODUCTION AND STUDY OBJECTIVES The accumuated earnings tax and the persona hoding company tax are penaty taxes designed to prevent taxpayers
More informationThe width of single glazing. The warmth of double glazing.
Therma Insuation CI/SfB (31) Ro5 (M5) September 2012 The width of singe gazing. The warmth of doube gazing. Pikington Spacia Revoutionary vacuum gazing. Image courtesy of Lumen Roofight Ltd. Pikington
More informationTCP/IP Gateways and Firewalls
Gateways and Firewas 1 Gateways and Firewas Prof. Jean-Yves Le Boudec Prof. Andrzej Duda ICA, EPFL CH-1015 Ecubens http://cawww.epf.ch Gateways and Firewas Firewas 2 o architecture separates hosts and
More informationBest Practices for Push & Pull Using Oracle Inventory Stock Locators. Introduction to Master Data and Master Data Management (MDM): Part 1
SPECIAL CONFERENCE ISSUE THE OFFICIAL PUBLICATION OF THE Orace Appications USERS GROUP spring 2012 Introduction to Master Data and Master Data Management (MDM): Part 1 Utiizing Orace Upgrade Advisor for
More informationAnnotated bibliographies for presentations in MUMT 611, Winter 2006
Stephen Sinclair Music Technology Area, McGill University. Montreal, Canada Annotated bibliographies for presentations in MUMT 611, Winter 2006 Presentation 4: Musical Genre Similarity Aucouturier, J.-J.
More informationSpatio-Temporal Asynchronous Co-Occurrence Pattern for Big Climate Data towards Long-Lead Flood Prediction
Spatio-Tempora Asynchronous Co-Occurrence Pattern for Big Cimate Data towards Long-Lead Food Prediction Chung-Hsien Yu, Dong Luo, Wei Ding, Joseph Cohen, David Sma and Shafiqu Isam Department of Computer
More informationChapter 2 Traditional Software Development
Chapter 2 Traditiona Software Deveopment 2.1 History of Project Management Large projects from the past must aready have had some sort of project management, such the Pyramid of Giza or Pyramid of Cheops,
More informationInformation Processing and Management
Information Processing and Management 45 (2009) 427 437 Contents ists avaiabe at ScienceDirect Information Processing and Management journa homepage: www.esevier.com/ocate/infoproman A systematic anaysis
More informationeffect on major accidents
An Investigation into a weekend (or bank hoiday) effect on major accidents Nicoa C. Heaey 1 and Andrew G. Rushton 2 1 Heath and Safety Laboratory, Harpur Hi, Buxton, Derbyshire, SK17 9JN 2 Hazardous Instaations
More informationLeadership & Management Certificate Programs
MANAGEMENT CONCEPTS Leadership & Management Certificate Programs Programs to deveop expertise in: Anaytics // Leadership // Professiona Skis // Supervision ENROLL TODAY! Contract oder Contract GS-02F-0010J
More informationSTRUCTURING WAYFINDING TASKS WITH IMAGE SCHEMATA
STRUCTURING WAYFINDING TASKS WITH IMAGE SCHEMATA By Martin M. Rauba A THESIS Submitted in Partia Fufiment of the Requirements for the Degree of Master of Science (in Spatia Information Science and Engineering)
More informationScheduling in Multi-Channel Wireless Networks
Scheduing in Muti-Channe Wireess Networks Vartika Bhandari and Nitin H. Vaidya University of Iinois at Urbana-Champaign, USA vartikab@acm.org, nhv@iinois.edu Abstract. The avaiabiity of mutipe orthogona
More informationThe Simple Pendulum. by Dr. James E. Parks
by Dr. James E. Parks Department of Physics and Astronomy 401 Niesen Physics Buidin The University of Tennessee Knoxvie, Tennessee 37996-100 Copyriht June, 000 by James Edar Parks* *A rihts are reserved.
More informationSoftware Quality - Getting Right Metrics, Getting Metrics Right
Software Quaity - Getting Right Metrics, Getting Metrics Right How to set the right performance metrics and then benchmark it for continuous improvement? Whie metrics are important means to quantify performance
More information