CIICT 2009 Proceedings

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Furthermore, the decode experiment in this case representeda single iteration of a bit sequence over the length of asignal. Given that any real world use of such a schemewould enable a bit sequence to be embedded repeatedly in acover signal, it would be possible to increase theeffectiveness of the decode process by, for example,repeated decoding and using the mode of the results.6.0 CONCLUSIONWe have proposed an application that utilises the superresolutioncapabilities of the CSPE algorithm to accuratelyidentify individual components of an audio signal, calculatetheir magnitudes and then alter magnitude as appropriate torepresent a particular bit value.Audio Engineering Society 36 th International Conference,Michigan, USA. June 2-4, <strong>2009</strong> (in press)[6] K. M. Short and R. A. Garcia, “Signal Analysis usingthe Complex Spectral Phase Evolution (CSPE) Method”,Audio Engineering Society 120 th Convention, May 2006,Paris, France[7] Douglas Nelson, “Cross Spectral Methods forProcessing Speech”, Journal of the Acoustic Society ofAmerica, vol. 110, No.5, pt.1, Nov.2001, pp.2575-2592[8] The online Webster Dictionary. http://www.websterdictionary.net/definition/interpolationExperimental tests using 500 synthesised signalsincorporating multiple randomly generated componentsembedded with a bit sequence of length 150 showed anaccuracy of completely perfect decoding of 99.2% with anaverage overall accuracy of 99.999%.Future work will determine how to calculate and set themagnitude so signal watermarking is perceptually invisible,by evaluating whether to modify the component to the leftor right of the user-defined frequency value, or both.Also, the impact of accidental and deliberate attacks on thewatermarked signal will be evaluated.7.0 REFERENCE[1] Merriam-Webster Online Dictionary.http://www.merriam-webster.com/dictionary/steganography[2] S. A. Craver, M. Wu, and B. Liu, “Reading between thelines: Lessons from the SDMI challenge,” in 10th USENIXSecurity Symposium. Washington, DC, 2001.[3] Moulin, P., & Koetter, R., “Data-Hiding Codes”, Proc.Of the IEEE, Vol. 93, No. 12, Dec. 2005.[4] Gopalan, K., et al, ‘Covert Speech Communication ViaCover Speech By Tone Insertion’, Proc. of the 2003 IEEEAerospace Conference, Big Sky, MT, March 2003.[5] Healy, R. & Timoney, J. ‘Digital Audio Watermarkingwith Semi-Blind Detection For In-Car Music Identification’40
USING CONVOLUTIVE NON-NEGATIVE MATRIX FACTORIZATIONALGORITHM TO PERFORM ACOUSTIC ECHO CANCELLATIONXIN ZHOU, YE LIANG, NIALL CAHILL, ROBERT LAWLORDepartment of Electronic Engineering, National University of Ireland Maynooth,Maynooth, Co. Kildare, Ireland.Email: zhou.xin@nuim.ie, liang.ye@nuim.ie, ncahill@eeng.nuim.ie, rlawlor@eeng.nuim.ieIn this paper Convolutive Non-negative Matrix Factorization (NMF) is used to perform acoustic echo cancellation. Thismodified version of NMF employs the idea of convolutive basis decomposition. CNMF makes use of a single basisfunction which spans the pattern length. This version of NMF allows us to reveal the underlying patterns which crossmultiple columns as single bases. We use this idea, realize it in the simulation environment and implement it to performAEC. This approach is evaluated through experiments on simulated data, from the results we find acoustic echo can bereduced using this approach.1. INTRODUCTIONIn recent years the use of hands free communication hasincreased significantly. A problem with such hands freecommunication is the occurrence of acoustic echo.Acoustic echo is an important issue for hands freecommunication. Acoustic echo exists in anycommunications scenario where there is a speaker and amicrophone, e.g. hands-free car phone, hands-freeconference phone, etc. Acoustic echo arises when soundfrom a loudspeaker is picked up by the microphone inthe same room. When the far-end user speaks to themicrophone, the microphone of the near-end user cancatch the output speech from the near-end speaker andretransmit back to the far-end user side. Usually theloudspeaker-enclosure-microphone coupling (LEM) ispresented as a time invariant linear FIR filter h(t),however as a small changes in the enclosureenvironment can greatly affect the LEM filter, such asopen or close a door, it is necessary to use an adaptiveLEM filter to model the echo path over time. Call thefar-end participant voice signal x(t), the near-end speechsignal v(t), and the noise n(t), then the echo signal y(t)can be stated as:N 1 (1)y( t) n( t) v( t) h( i) x( t i)i0Here N is the length of the impulse response and t is thetime index.Most existing AEC techniques use Least Mean Square(LMS) or Normalized LMS (NLMS) [1] to estimate andupdate the LEM filter coefficients. It works bycalculating an estimate of the acoustic echo based on thereference speech and the incoming captured speechsignal. This acoustic echo estimate is then subtractedfrom the near-end speech before being sent to the farenduser.There exist some problems to this approach: first of all,long estimation filters are needed for a LEM filter. Theresulting long impulse responses can lead toconvergence issues and large computation load.Secondly, the adaptive algorithm may diverge if thereference signal contains noise. Thirdly, changes in theLEM filter may lead to a period of misadjustment.Finally, the adaptive algorithm may diverge away fromsuitable FIR coefficients when both near-end and farendusers speak simultaneously [2].In order to overcome these problems, a number oftechniques have been developed [1]. However, theacoustic echo problem has not yet to be solvedcompletely. In this paper an alternative approach toAEC is presented. A monaural sound source separation(SSS) technique based on Convolutive non-negativematrix factorisation (CNMF) is employed to performAEC. It is shown that this approach can lead tosignificant echo reduction particularly duringdoubletalk.The structure of the paper is as follows. In section 2Monaural Sound Source Separation is described,sections 3 and 4 introduce the theory of NMF andCNMF. In section 5 we describe the experimentsmaking use of CNMF to perform AEC followed byresults and discussion in sections 6.2. MONAURAL SOUND SEPARATIONMonaural sound source separation is a technique thatattempts to separate any number of sound sources withonly one mixture of the sources. As only one mixture141
Page 3 and 4: CIICT 2009Proceedings of the China-
Page 5 and 6: ForewordOn behalf of the organising
Page 7 and 8: TABLE OF CONTENTSSection 1A: ANTENN
Page 9 and 10: Section 3B: COMPUTER VISION 133A ne
Page 11 and 12: Section 1AANTENNAS AND CIRCUITS 11
Page 13 and 14: A block diagram of the platform is
Page 15 and 16: These two paths are averaged to red
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Page 20 and 21: The results show that the separatio
Page 22 and 23: same size of annular-ring outer rad
Page 25 and 26: [4] C.Y.Sim, K.W.Lin, J.S.Row, Desi
Page 27 and 28: Interpretation of Spatial Movement
Page 30 and 31: III.DATA COLLECTIONAccurate spatial
Page 32 and 33: navigation. We define a walking are
Page 34 and 35: one’s body position, movement and
Page 36 and 37: world and will result in them being
Page 38 and 39: GPS/RFID/Self-contained Sensor Syst
Page 40 and 41: When the headway measures are equal
Page 42 and 43: [4] R.Liu, and S. Sinha, “Modelli
Page 44 and 45: Digital Audio Watermarking by Magni
Page 46 and 47: j 2 πβND = e(2)The frequency depe
Page 48 and 49: accurate in identifying the compone
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Page 54 and 55: iteration, one NMF for each frame.
Page 56 and 57: 7. CONCLUSIONSIn this paper we empl
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Page 60 and 61: length of x 1[n ], while it only ap
Page 62 and 63: Computing Modified Bessel functions
Page 64 and 65: From (9) both the numerator and den
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Page 68 and 69: puncturing some parity RSDT columns
Page 70 and 71: [5] O'Droma M. S. and I. Ganchev.
Page 72 and 73: Administrations (CEPT) has proposed
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Page 76 and 77: ACKNOWLEDGEMENTSThe authors wish to
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Page 92 and 93: vol. 6, no. 3, pp. 30-38, 2007.[11]
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Spatial - temporal Simulation and P
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and serious ones in turn. Overall,
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[3] Cheng Weiming, Zhou Chenghu, an
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(a) 1973 (b) 1990 (c) 2000Figure 1.
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Effect of Hard RTOS on DPDC SCADA S
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Therefore, this solution is not fea
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already done on error free system o
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8 PROPOSED REAL-TIME OPERATING SYST
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HYBRID DECODING SCHEMES FOR TURBO-C
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L a(u k ) SOVA/Log-MAPLog-MAP Inter
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JavaScript code Fragment Analyzingf
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The external JavaScript itself, whi
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Section 3ACHANNELS AND PROPAGATION1
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The DCF is assumed to have negligib
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When m=6, the fluctuation have been
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Across a 5 MHz spectrum, the maximu
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Fig 6 shows the time variant charac
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In the system, the RF signal is dir
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of the different speeds UWB standar
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3dB higher for the laser biased at
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information for inter domain pre-au
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In addition, the opendiameter imple
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A new algorithm of edge detection b
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Control structure elements of shape
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3D TOOTH RECONSTRUCTIONWITH MULTIPL
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3.1 Topological structure and base
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Automatic Recognition of Head Movem
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threshold model λ is created to ca
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Dirt and Sparkle Detection for Film
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[4] T. Komatsu, T. Saito, “Detect
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Lightweight Signal Processing Algor
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a two element PIR sensor, to obtain
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the sensor node can readily be chec
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parameters may not lead to simple a
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0 (1 dR1)(1 dR2) 1[1]where d is t
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epeatability is normally defined as
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Integrated air quality monitoring:
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colour coded data that are used to
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Approximate Analysis of Fibre Delay
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Virtual Traffic SourceA*f( t)= A* e
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Section 4AANTENNAS AND CIRCUITS 217
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Primary SpiralSecondary SpiralCoCbL
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[4] A H Miklich, J X Przybysz, T J
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ased on the ABCD transmission matri
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RRe( Y3 _ LowFreq)=s(19)2 2 2Rs+ ω
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Section 4BeLearning180
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were investigated as important inpu
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Time [min]1009080706050403020100Loc
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Mean Rating for Different Encoding
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Billing Issues when Accessing Perso
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data consumed, for example 0.2€/M
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and returns billing plans the learn
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lecture size and the price per quan
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Section 5ARADIO SYSTEMS 2196
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and is uniformly distributed in the
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When the number of users is 2, and
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III.BLUETOOTH OPERATIONS IN SNIFF M
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traffic or for POLL and NULL packet
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Necessity for an Intelligent Bandwi
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Spruce: Spruce has been one of the
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Fig. 5 Comparison of bandwidth calc
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Section 5BCOMPUTER NETWORKS 2212
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2.1 CNFThe Content Navigating Funct
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User BGF PD-FE CSCF CNF(AS) HSSPeer
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5 ConclusionThis paper proposed an
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d( X − X ) + ( Y −Y)= ∑ ∑2
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Program Dependence Graph Generation
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CDGPDGFigure 2. Program Dependence
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Section 6ADIGITAL HOLOGRAPHY226
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away. Immediately behind this plane
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While this is not ideal as hair is
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Figure 12. 3-D visualisation of 200
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Speed up of Fresnel transforms for
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[9]L. Onural and H. Ozaktas, "Signa
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2009 China-Ireland International Co
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2009 China-Ireland International Co
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that enables the complete separatio
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developed in the literature to remo
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Section 6BINTELLIGENT SYSTEMS246
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the vectors remain just form the in
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d d x,y . If x and y don’t belon
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Investigating the Influence of Popu
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TABLE I.EXPERIMENTAL RESULTS FOR 26
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Intelligent Learning Systems Where
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FuzzyLogicBayesianReasoningClassifi
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AN IMPROVED HAPTIC ALGORITHM FOR VI
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Fig. 2. Proxy ImmersionFig. 1. Prox
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1Corpus Design Techniques for Irish
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