# Applying the Filtered Back-Projection Method to Extract Signal at Specific Position

Size: px
Start display at page:

Download "Applying the Filtered Back-Projection Method to Extract Signal at Specific Position"

## Transcription

2 2 Figure 3: The Radon geometry Figure 2: The Radon geometry = f(x, y)δ(x cos θ + y sin θ s) dxdy, (4) where < s < and θ < π. The symbol R, denoting the Radon transform operator, is also called the projection operator. In the rotated coordinate system (s, u), the relationship to (x, y) are s = x cos θ + y sin θ (5) u = x sin θ + y cos θ. (6) Associated with the Radon transform is the backprojection operator denoted as β and is defined as b(x, y) = β{g(s, θ)} = g(x cos θ + y sin θ, θ) dθ. (7) The quantity b(x, y) is called the back projection of g(s, θ). In polar coordinates it can be written as b p (s, φ) = g(r cos(θ φ), θ) dθ (8) Eqs. (7) and (8) represent the accumulation of the raysums of all of the rays that pass through the point (x, y). For example, if g(s, θ) = g 1 (s)δ(θ θ 1 ) + g 2 (s)δ(θ θ 2 ) (9) that is, there are only two projections, then b p (r, φ) = g 1 (s 1 ) + g 2 (s 2 ), (1) where s 1 = r cos(θ 1 φ) and s 2 = r cos(θ 2 φ). In general, for a fixed point (x, y) or (r, φ), the value of back projection β{g} is evaluated by integrating g(s, θ) over θ for all lines pass through that point. The one-dimensional Fourier transform with respect to s of the projection g(s, θ) can be obtained from Eq. (4) G θ = F 1 {g θ (s)} = f(s cos θ u sin θ, s sin θ + u cos θ) e j2πrs dsdu (11) where F 1 is the one-dimensional forward Fourier transformation and G θ (r) is the Fourier transform of g θ (s). Rotating the coordinate from {s, u} to {x, y} then Eq. (11) becomes G θ (r) = f(x, y)e j2π(xr cos θ+yr sin θ) dxdy = F (r cos θ, r sin θ) (12) where F (r cos θ, r sin θ) is a slice of two-dimensional Fourier transform of tomogram at angle θ as shown in Fig. 3. If θ < π and < r <, F (r cos θ, r sin θ) represents the two-dimensional Fourier transform of the tomogram in polar form. The tomogram can be obtained by applying the two-dimensional inverse Fourier transform in polar form, that is, F (r cos θ, r sin θ) r e j2πr(x cos θ+y sin θ) drdθ (13) where dr represents the one-dimensional inverse Fourier transform, g dθ denotes the back projection as Eq. (8), and F (r cos θ, r sin θ) r represent the filter operation in spatial domain. Combining the equations above, we have F 1 1 {F 1{g θ (s)} r } dθ, (14) where g θ (s) is the projection of f(x, y) at angle θ, the F 1 and F1 1 are the one-dimensional Fourier and inverse Fourier transforms, respectively. And r is the backprojection filter in frequency domain and s = x cos θ + y sin θ. The slice information we have are discrete in angle. Then, the Eq. (14) in discrete form is F1 1 {F 1{g θ (s)} r }. (15) θ<π Therefore, profile of the object, that is f(x, y), could be reconstructed by summation of each filtered slice g θ (s). 3. Sound Projection We may consider that if the sound signal received by each microphone is a kind of projection, a non-straight projection, unlike the x-ray is doing. Then we can use the

3 3 Figure 4: The sound projection. back-projection technique to reconstruct the sound source signal. The signals come from point sources located on the concentric circules are received by microphones at the same time if the microphones is at the center. In other word, if the distances from sources to one microphone are the same, signal from different sources on the same concentric circule reach to the microphone at the same time. Fig. 4 shows the relation between distance from microphone and received time, where d k is the radius from center, the position of microphone, and t k is the time that wave propagated to microphone. The received signals at time T +t k is the summation on radius d k and is multiplied by a constant attenuation coefficient. We may consider that would exist a sound projection slice theorem, then the back-projection operation of sound could be rewrite from Eq. (15) and obtain s(t ) = k<m F 1 1 {F 1{m k (T )} ω }, (16) where T is a time period, s(t) is the source signal, m k (t) is the signal received by the kth microphone, ω is the back-projection filter in frequency domain, and M is the size of microphone array. Next, we use the Eq. (16) to separate the source we focus on from mixed-signal received by microphone array. 4. Implementation For the limitation of hardware, we use Matlab to simulate all of the system. The system structure is shown in Fig. 5. Assume there is a conference room with 4 meters width and 5 meters length without reverberant. A 16-channel microphone array is linearly arranged on the bottom side. Two point sources could be placed at any place in this room. The sine wave is used as the sound source in the begining of simulation. The main part of the program is the filtered backprojection operation. The sequence is choosing the proper region of signal received from each microphone and filtering it, then average the filtered signal of each microphone in time domain like delay and sum method. In Fig. 6, the simulation of filtered back-projection method with two sine wave signal sources is shown. The signals of two sources is the same as shown at the up-left plot in Fig. 6. The microphone receive the mixed-signal from source (the signal is interested) and noise (the signal isn t interested) is shown in bottom-left plot in Fig. 6. The Figure 5: System architecture. Figure 6: Simulation of filtered back-projection method with two sound sources. result in spacial domain is shown at bottom-right plot in Fig. 6. The equation used to measure the efficiency to extract the interesting signal is shown as ( ) s SNR signal = 2 log (db), (17) s r where s and r are the signals of the source and extraction result, respectively. This measurement shows the extracted signal compares with original signal. Another measurement is used to measure the capability to reduce noisy signal is shown as ( ) r s SNR noise = 2 log (db), (18) s where s and r are the signals of the source and extraction result, respectively, and s denotes the noisy signal. The simulation results of sound source extraction are presented. First, sources in different frequencies are sim-

4 4 Figure 7: Simulation of two sound sources with two differ- Figure 8: Simulation of two single frequency sources with ent frequency using filtered back-projection method with. signal fixed position and noise changed. The higher value ulated. Second, experimation with changing the microphone array size and arrangement are shown. Finally, two pieces of real music are mixed and separated. First, we verify the algorithm with two different frequencies in each source. The arrangement of components is the same as above. Let S1 and S2 are two dual frequency sources. The frequencies of S1 are 1135 and 341 Hz. The frequencies of S2 are 14 and 238 Hz. In the Fig. 7 the spectrums of all the signal are shown. The top two figures are the spectrum of S1 and S2. The middle two figures are the signal received by microphone, m8. The left side figure: S1 is treated as the signal that we want then S2 is noise. On the contrary, the right side figure S2 is treated as signal. In the result, the magnitude of low frequency is smaller then high frequency. This is due to the filter we used is the high pass filter. Next, we verify the efficiency and the sources position relation in two scenarios. Two point sources are placed in the conference room. One is treated as signal and the other is treated as interference. The signal source is placed at the fixed position and interference source is moved in the room area. When noise located at each point, SNRsignal and SNRnoise are calculated. The moving step of interference is 1 cm. In order to show the tendency of extraction efficiency, room size is changed to 6 6 meters and microphone array is still placed at the same place with 4 meters width. The single frequency and multi-frequency results are shown in Figs. 8 and 9. The second scenario, we let the interference at a fixed position and move source in the room. Then also calculate the SNR with the same condition. Figs. 1 and 11 are the result of the single and multiple frequencies, respectively. From the four figures above, we could see the dark part means the simulation could extract signal we want clearly when signal source is in front of the noise source and near the microphone array. On the contrary, when signal is near the noise or far from to microphone array, the signal to noise ratio is less than 2 db. At final, we use two pieces of music in the simulation. The S1 is female voice song and S2 is the male voice song. The simulation system extract S1 and S2 from the mixed signal received by the 16-channel microphone array with the filtered back-projection method we proposed and Figure 9: Simulation of two dual frequencies sources with signal fixed position and noise changed. The higher value delay-and-sum method. Choosing 3 periods of.5 second signal form two result music which is 3 seconds long to calculate the SNR. The comparion between filtered backprojection method and delay-and-sum method is shown in Table Conclusion The simulation result shows the filtered back-projection method may reduce the noise and enhance the signal we wantt. As shown in figures, when the difference of distance from sources to microphone array in a proper range, the signal from the focus source could be extracted clearly. This system could be used in teleconference. Usually, the position of conferees is in front of the screen. According the Eq. 15, microphone array should be arranged around the half room. We consider the realization of the system would be too complex to setup hardware on multi-walls, so only arrange the microphone array under the projection screen. It would be a proper position to receive voice from speaker s height when sitting.

5 5 Figure 1: Simulation of two single frequency sources with noise fixed position and signal changed. The higher value Figure 11: Simulation of two dual frequencies sources with noise fixed position and signal changed. The higher value Table 1: SNR signal (db) of extraction of music using two methods. The larger value Back- Delay- Time projection -and-sum S 1 S 2 S 1 S 2 t t t t t Average References [1] H. F. Silverman, W. R. Patterson, and J. L. Flanagan, The huge microphone array, IEEE Trans. on Concurrency, vol. 6, no. 4, pp , Oct.-Dec [2] Y. Tamai, S. Kagami, H. Mizoguchi, K. Sakaya, K. Nagashima, and T. Takano, Circular microphone array for meeting system, vol. 2, pp [3] D. Giuliani, M. Matassoni, and M. Omologo, Hands free continuous speech recognition in noisy environment using a four microphone array, in Proc. of IEEE Int l. Conf. on Acoustics, Speech, and Signal Processing 1995 (ICASSP 95), vol. 1, Detroit, MI, May 1995, pp [4] G. T. Herman, Ed., Image Reconstruction from Projections: Implementation and Applications. Berlin: Springer-Verlag, 1979.

### Estimation of Sinusoidally Modulated Signal Parameters Based on the Inverse Radon Transform

Estimation of Sinusoidally Modulated Signal Parameters Based on the Inverse Radon Transform Miloš Daković, Ljubiša Stanković Faculty of Electrical Engineering, University of Montenegro, Podgorica, Montenegro

### Mel Spectrum Analysis of Speech Recognition using Single Microphone

International Journal of Engineering Research in Electronics and Communication Mel Spectrum Analysis of Speech Recognition using Single Microphone [1] Lakshmi S.A, [2] Cholavendan M [1] PG Scholar, Sree

### Basic Signals and Systems

Chapter 2 Basic Signals and Systems A large part of this chapter is taken from: C.S. Burrus, J.H. McClellan, A.V. Oppenheim, T.W. Parks, R.W. Schafer, and H. W. Schüssler: Computer-based exercises for

### Speech and Audio Processing Recognition and Audio Effects Part 3: Beamforming

Speech and Audio Processing Recognition and Audio Effects Part 3: Beamforming Gerhard Schmidt Christian-Albrechts-Universität zu Kiel Faculty of Engineering Electrical Engineering and Information Engineering

### Digital Video and Audio Processing. Winter term 2002/ 2003 Computer-based exercises

Digital Video and Audio Processing Winter term 2002/ 2003 Computer-based exercises Rudolf Mester Institut für Angewandte Physik Johann Wolfgang Goethe-Universität Frankfurt am Main 6th November 2002 Chapter

### Localization of underwater moving sound source based on time delay estimation using hydrophone array

Journal of Physics: Conference Series PAPER OPEN ACCESS Localization of underwater moving sound source based on time delay estimation using hydrophone array To cite this article: S. A. Rahman et al 2016

### Broadband Microphone Arrays for Speech Acquisition

Broadband Microphone Arrays for Speech Acquisition Darren B. Ward Acoustics and Speech Research Dept. Bell Labs, Lucent Technologies Murray Hill, NJ 07974, USA Robert C. Williamson Dept. of Engineering,

### Non Unuiform Phased array Beamforming with Covariance Based Method

IOSR Journal of Engineering (IOSRJE) e-iss: 50-301, p-iss: 78-8719, Volume, Issue 10 (October 01), PP 37-4 on Unuiform Phased array Beamforming with Covariance Based Method Amirsadegh Roshanzamir 1, M.

### Adaptive Fingerprint Binarization by Frequency Domain Analysis

Adaptive Fingerprint Binarization by Frequency Domain Analysis Josef Ström Bartůněk, Mikael Nilsson, Jörgen Nordberg, Ingvar Claesson Department of Signal Processing, School of Engineering, Blekinge Institute

### Lab S-3: Beamforming with Phasors. N r k. is the time shift applied to r k

DSP First, 2e Signal Processing First Lab S-3: Beamforming with Phasors Pre-Lab: Read the Pre-Lab and do all the exercises in the Pre-Lab section prior to attending lab. Verification: The Exercise section

### Audio Imputation Using the Non-negative Hidden Markov Model

Audio Imputation Using the Non-negative Hidden Markov Model Jinyu Han 1,, Gautham J. Mysore 2, and Bryan Pardo 1 1 EECS Department, Northwestern University 2 Advanced Technology Labs, Adobe Systems Inc.

### Single Channel Speaker Segregation using Sinusoidal Residual Modeling

NCC 2009, January 16-18, IIT Guwahati 294 Single Channel Speaker Segregation using Sinusoidal Residual Modeling Rajesh M Hegde and A. Srinivas Dept. of Electrical Engineering Indian Institute of Technology

### Audio Fingerprinting using Fractional Fourier Transform

Audio Fingerprinting using Fractional Fourier Transform Swati V. Sutar 1, D. G. Bhalke 2 1 (Department of Electronics & Telecommunication, JSPM s RSCOE college of Engineering Pune, India) 2 (Department,

### COMPUTATIONAL IMAGING. Berthold K.P. Horn

COMPUTATIONAL IMAGING Berthold K.P. Horn What is Computational Imaging? Computation inherent in image formation What is Computational Imaging? Computation inherent in image formation (1) Computing is getting

### Improving Meetings with Microphone Array Algorithms. Ivan Tashev Microsoft Research

Improving Meetings with Microphone Array Algorithms Ivan Tashev Microsoft Research Why microphone arrays? They ensure better sound quality: less noises and reverberation Provide speaker position using

### The Analysis of the Airplane Flutter on Low Band Television Broadcasting Signal

The Analysis of the Airplane Flutter on Low Band Television Broadcasting Signal A. Wonggeeratikun 1,2, S. Noppanakeepong 1, N. Leelaruji 1, N. Hemmakorn 1, and Y. Moriya 1 1 Faculty of Engineering and

### Robust Low-Resource Sound Localization in Correlated Noise

INTERSPEECH 2014 Robust Low-Resource Sound Localization in Correlated Noise Lorin Netsch, Jacek Stachurski Texas Instruments, Inc. netsch@ti.com, jacek@ti.com Abstract In this paper we address the problem

### Live multi-track audio recording

Live multi-track audio recording Joao Luiz Azevedo de Carvalho EE522 Project - Spring 2007 - University of Southern California Abstract In live multi-track audio recording, each microphone perceives sound

### WIND SPEED ESTIMATION AND WIND-INDUCED NOISE REDUCTION USING A 2-CHANNEL SMALL MICROPHONE ARRAY

INTER-NOISE 216 WIND SPEED ESTIMATION AND WIND-INDUCED NOISE REDUCTION USING A 2-CHANNEL SMALL MICROPHONE ARRAY Shumpei SAKAI 1 ; Tetsuro MURAKAMI 2 ; Naoto SAKATA 3 ; Hirohumi NAKAJIMA 4 ; Kazuhiro NAKADAI

### Chapter 1. Electronics and Semiconductors

Chapter 1. Electronics and Semiconductors Tong In Oh 1 Objective Understanding electrical signals Thevenin and Norton representations of signal sources Representation of a signal as the sum of sine waves

### Interference in stimuli employed to assess masking by substitution. Bernt Christian Skottun. Ullevaalsalleen 4C Oslo. Norway

Interference in stimuli employed to assess masking by substitution Bernt Christian Skottun Ullevaalsalleen 4C 0852 Oslo Norway Short heading: Interference ABSTRACT Enns and Di Lollo (1997, Psychological

### Introduction to signals and systems

CHAPTER Introduction to signals and systems Welcome to Introduction to Signals and Systems. This text will focus on the properties of signals and systems, and the relationship between the inputs and outputs

### About Doppler-Fizeau effect on radiated noise from a rotating source in cavitation tunnel

PROCEEDINGS of the 22 nd International Congress on Acoustics Signal Processing in Acoustics (others): Paper ICA2016-111 About Doppler-Fizeau effect on radiated noise from a rotating source in cavitation

### Omnidirectional Sound Source Tracking Based on Sequential Updating Histogram

Proceedings of APSIPA Annual Summit and Conference 5 6-9 December 5 Omnidirectional Sound Source Tracking Based on Sequential Updating Histogram Yusuke SHIIKI and Kenji SUYAMA School of Engineering, Tokyo

### Antennas and Propagation. Chapter 6b: Path Models Rayleigh, Rician Fading, MIMO

Antennas and Propagation b: Path Models Rayleigh, Rician Fading, MIMO Introduction From last lecture How do we model H p? Discrete path model (physical, plane waves) Random matrix models (forget H p and

### Study on Multi-tone Signals for Design and Testing of Linear Circuits and Systems

Study on Multi-tone Signals for Design and Testing of Linear Circuits and Systems Yukiko Shibasaki 1,a, Koji Asami 1,b, Anna Kuwana 1,c, Yuanyang Du 1,d, Akemi Hatta 1,e, Kazuyoshi Kubo 2,f and Haruo Kobayashi

### Room Impulse Response Modeling in the Sub-2kHz Band using 3-D Rectangular Digital Waveguide Mesh

Room Impulse Response Modeling in the Sub-2kHz Band using 3-D Rectangular Digital Waveguide Mesh Zhixin Chen ILX Lightwave Corporation Bozeman, Montana, USA Abstract Digital waveguide mesh has emerged

### Speech Enhancement Using Microphone Arrays

Friedrich-Alexander-Universität Erlangen-Nürnberg Lab Course Speech Enhancement Using Microphone Arrays International Audio Laboratories Erlangen Prof. Dr. ir. Emanuël A. P. Habets Friedrich-Alexander

### Lecture 3 Complex Exponential Signals

Lecture 3 Complex Exponential Signals Fundamentals of Digital Signal Processing Spring, 2012 Wei-Ta Chu 2012/3/1 1 Review of Complex Numbers Using Euler s famous formula for the complex exponential The

### Chapter 3 Data Transmission COSC 3213 Summer 2003

Chapter 3 Data Transmission COSC 3213 Summer 2003 Courtesy of Prof. Amir Asif Definitions 1. Recall that the lowest layer in OSI is the physical layer. The physical layer deals with the transfer of raw

### UNIT Explain the radiation from two-wire. Ans: Radiation from Two wire

UNIT 1 1. Explain the radiation from two-wire. Radiation from Two wire Figure1.1.1 shows a voltage source connected two-wire transmission line which is further connected to an antenna. An electric field

### SPEECH ENHANCEMENT USING A ROBUST KALMAN FILTER POST-PROCESSOR IN THE MODULATION DOMAIN. Yu Wang and Mike Brookes

SPEECH ENHANCEMENT USING A ROBUST KALMAN FILTER POST-PROCESSOR IN THE MODULATION DOMAIN Yu Wang and Mike Brookes Department of Electrical and Electronic Engineering, Exhibition Road, Imperial College London,

### Functions of more than one variable

Chapter 3 Functions of more than one variable 3.1 Functions of two variables and their graphs 3.1.1 Definition A function of two variables has two ingredients: a domain and a rule. The domain of the function

### Lecture 2: SIGNALS. 1 st semester By: Elham Sunbu

Lecture 2: SIGNALS 1 st semester 1439-2017 1 By: Elham Sunbu OUTLINE Signals and the classification of signals Sine wave Time and frequency domains Composite signals Signal bandwidth Digital signal Signal

### arxiv: v1 [cs.sd] 4 Dec 2018

LOCALIZATION AND TRACKING OF AN ACOUSTIC SOURCE USING A DIAGONAL UNLOADING BEAMFORMING AND A KALMAN FILTER Daniele Salvati, Carlo Drioli, Gian Luca Foresti Department of Mathematics, Computer Science and

### SPECTRAL COMBINING FOR MICROPHONE DIVERSITY SYSTEMS

17th European Signal Processing Conference (EUSIPCO 29) Glasgow, Scotland, August 24-28, 29 SPECTRAL COMBINING FOR MICROPHONE DIVERSITY SYSTEMS Jürgen Freudenberger, Sebastian Stenzel, Benjamin Venditti

### Speech Enhancement Using Beamforming Dr. G. Ramesh Babu 1, D. Lavanya 2, B. Yamuna 2, H. Divya 2, B. Shiva Kumar 2, B.

www.ijecs.in International Journal Of Engineering And Computer Science ISSN:2319-7242 Volume 4 Issue 4 April 2015, Page No. 11143-11147 Speech Enhancement Using Beamforming Dr. G. Ramesh Babu 1, D. Lavanya

### Automotive three-microphone voice activity detector and noise-canceller

Res. Lett. Inf. Math. Sci., 005, Vol. 7, pp 47-55 47 Available online at http://iims.massey.ac.nz/research/letters/ Automotive three-microphone voice activity detector and noise-canceller Z. QI and T.J.MOIR

### COMMUNICATION SYSTEMS NCERT

Exemplar Problems Physics Chapter Fifteen COMMUNCATON SYSTEMS MCQ 151 Three waves A, B and C of frequencies 1600 khz, 5 MHz and 60 MHz, respectively are to be transmitted from one place to another Which

### University Ibn Tofail, B.P. 133, Kenitra, Morocco. University Moulay Ismail, B.P Meknes, Morocco

Research Journal of Applied Sciences, Engineering and Technology 8(9): 1132-1138, 2014 DOI:10.19026/raset.8.1077 ISSN: 2040-7459; e-issn: 2040-7467 2014 Maxwell Scientific Publication Corp. Submitted:

### Terminology (1) Chapter 3. Terminology (3) Terminology (2) Transmitter Receiver Medium. Data Transmission. Direct link. Point-to-point.

Terminology (1) Chapter 3 Data Transmission Transmitter Receiver Medium Guided medium e.g. twisted pair, optical fiber Unguided medium e.g. air, water, vacuum Spring 2012 03-1 Spring 2012 03-2 Terminology

### Introduction to Digital Signal Processing (Discrete-time Signal Processing)

Introduction to Digital Signal Processing (Discrete-time Signal Processing) Prof. Chu-Song Chen Research Center for Info. Tech. Innovation, Academia Sinica, Taiwan Dept. CSIE & GINM National Taiwan University

### Sound Source Localization using HRTF database

ICCAS June -, KINTEX, Gyeonggi-Do, Korea Sound Source Localization using HRTF database Sungmok Hwang*, Youngjin Park and Younsik Park * Center for Noise and Vibration Control, Dept. of Mech. Eng., KAIST,

### COMP 546, Winter 2017 lecture 20 - sound 2

Today we will examine two types of sounds that are of great interest: music and speech. We will see how a frequency domain analysis is fundamental to both. Musical sounds Let s begin by briefly considering

### Discrete Fourier Transform (DFT)

Amplitude Amplitude Discrete Fourier Transform (DFT) DFT transforms the time domain signal samples to the frequency domain components. DFT Signal Spectrum Time Frequency DFT is often used to do frequency

### Measuring impulse responses containing complete spatial information ABSTRACT

Measuring impulse responses containing complete spatial information Angelo Farina, Paolo Martignon, Andrea Capra, Simone Fontana University of Parma, Industrial Eng. Dept., via delle Scienze 181/A, 43100

### Different Approaches of Spectral Subtraction Method for Speech Enhancement

ISSN 2249 5460 Available online at www.internationalejournals.com International ejournals International Journal of Mathematical Sciences, Technology and Humanities 95 (2013 1056 1062 Different Approaches

### Laboratory Assignment 4. Fourier Sound Synthesis

Laboratory Assignment 4 Fourier Sound Synthesis PURPOSE This lab investigates how to use a computer to evaluate the Fourier series for periodic signals and to synthesize audio signals from Fourier series

### Outline / Wireless Networks and Applications Lecture 3: Physical Layer Signals, Modulation, Multiplexing. Cartoon View 1 A Wave of Energy

Outline 18-452/18-750 Wireless Networks and Applications Lecture 3: Physical Layer Signals, Modulation, Multiplexing Peter Steenkiste Carnegie Mellon University Spring Semester 2017 http://www.cs.cmu.edu/~prs/wirelesss17/

### A comparative study on main lobe and side lobe of frequency response curve for FIR Filter using Window Techniques

Proc. of Int. Conf. on Computing, Communication & Manufacturing 4 A comparative study on main lobe and side lobe of frequency response curve for FIR Filter using Window Techniques Sudipto Bhaumik, Sourav

### Structure of Speech. Physical acoustics Time-domain representation Frequency domain representation Sound shaping

Structure of Speech Physical acoustics Time-domain representation Frequency domain representation Sound shaping Speech acoustics Source-Filter Theory Speech Source characteristics Speech Filter characteristics

### Direction-of-Arrival Estimation Using a Microphone Array with the Multichannel Cross-Correlation Method

Direction-of-Arrival Estimation Using a Microphone Array with the Multichannel Cross-Correlation Method Udo Klein, Member, IEEE, and TrInh Qu6c VO School of Electrical Engineering, International University,

### Source Localisation Mapping using Weighted Interaural Cross-Correlation

ISSC 27, Derry, Sept 3-4 Source Localisation Mapping using Weighted Interaural Cross-Correlation Gavin Kearney, Damien Kelly, Enda Bates, Frank Boland and Dermot Furlong. Department of Electronic and Electrical

### KULLIYYAH OF ENGINEERING

KULLIYYAH OF ENGINEERING DEPARTMENT OF ELECTRICAL & COMPUTER ENGINEERING ANTENNA AND WAVE PROPAGATION LABORATORY (ECE 4103) EXPERIMENT NO 3 RADIATION PATTERN AND GAIN CHARACTERISTICS OF THE DISH (PARABOLIC)

### Part 1: Standing Waves - Measuring Wavelengths

Experiment 7 The Microwave experiment Aim: This experiment uses microwaves in order to demonstrate the formation of standing waves, verifying the wavelength λ of the microwaves as well as diffraction from

### Sampling and Reconstruction of Analog Signals

Sampling and Reconstruction of Analog Signals Chapter Intended Learning Outcomes: (i) Ability to convert an analog signal to a discrete-time sequence via sampling (ii) Ability to construct an analog signal

### A MICROPHONE ARRAY INTERFACE FOR REAL-TIME INTERACTIVE MUSIC PERFORMANCE

A MICROPHONE ARRA INTERFACE FOR REAL-TIME INTERACTIVE MUSIC PERFORMANCE Daniele Salvati AVIRES lab Dep. of Mathematics and Computer Science, University of Udine, Italy daniele.salvati@uniud.it Sergio Canazza

### IMPULSE RESPONSE MEASUREMENT WITH SINE SWEEPS AND AMPLITUDE MODULATION SCHEMES. Q. Meng, D. Sen, S. Wang and L. Hayes

IMPULSE RESPONSE MEASUREMENT WITH SINE SWEEPS AND AMPLITUDE MODULATION SCHEMES Q. Meng, D. Sen, S. Wang and L. Hayes School of Electrical Engineering and Telecommunications The University of New South

### Off-axis response of Compton and photoelectric polarimeters with a large field of view

Off-axis response of Compton and photoelectric polarimeters with a large field of view Fabio Muleri fabio.muleri@iaps.inaf.it X-ray polarisation in astrophysics -a window about to open? Stockholm, Sweden,

### 1. Measure angle in degrees and radians 2. Find coterminal angles 3. Determine the arc length of a circle

Pre- Calculus Mathematics 12 5.1 Trigonometric Functions Goal: 1. Measure angle in degrees and radians 2. Find coterminal angles 3. Determine the arc length of a circle Measuring Angles: Angles in Standard

### Signal Processing. Naureen Ghani. December 9, 2017

Signal Processing Naureen Ghani December 9, 27 Introduction Signal processing is used to enhance signal components in noisy measurements. It is especially important in analyzing time-series data in neuroscience.

### Approaches for Angle of Arrival Estimation. Wenguang Mao

Approaches for Angle of Arrival Estimation Wenguang Mao Angle of Arrival (AoA) Definition: the elevation and azimuth angle of incoming signals Also called direction of arrival (DoA) AoA Estimation Applications:

### Performance Analysis of Parallel Acoustic Communication in OFDM-based System

Performance Analysis of Parallel Acoustic Communication in OFDM-based System Junyeong Bok, Heung-Gyoon Ryu Department of Electronic Engineering, Chungbuk ational University, Korea 36-763 bjy84@nate.com,

### Joint Position-Pitch Decomposition for Multi-Speaker Tracking

Joint Position-Pitch Decomposition for Multi-Speaker Tracking SPSC Laboratory, TU Graz 1 Contents: 1. Microphone Arrays SPSC circular array Beamforming 2. Source Localization Direction of Arrival (DoA)

### 11/8/2007 Antenna Pattern notes 1/1

11/8/27 ntenna Pattern notes 1/1 C. ntenna Pattern Radiation Intensity is dependent on both the antenna and the radiated power. We can normalize the Radiation Intensity function to construct a result that

### DESIGN AND APPLICATION OF DDS-CONTROLLED, CARDIOID LOUDSPEAKER ARRAYS

DESIGN AND APPLICATION OF DDS-CONTROLLED, CARDIOID LOUDSPEAKER ARRAYS Evert Start Duran Audio BV, Zaltbommel, The Netherlands Gerald van Beuningen Duran Audio BV, Zaltbommel, The Netherlands 1 INTRODUCTION

### Image Simulator for One Dimensional Synthetic Aperture Microwave Radiometer

524 Progress In Electromagnetics Research Symposium 25, Hangzhou, China, August 22-26 Image Simulator for One Dimensional Synthetic Aperture Microwave Radiometer Qiong Wu, Hao Liu, and Ji Wu Center for

### Electromagnetic Spectrum

Electromagnetic Spectrum The electromagnetic radiation covers a vast spectrum of frequencies and wavelengths. This includes the very energetic gamma-rays radiation with a wavelength range from 0.005 1.4

### Signal Characteristics

Data Transmission The successful transmission of data depends upon two factors:» The quality of the transmission signal» The characteristics of the transmission medium Some type of transmission medium

### Name Date Class. Identify whether each function is periodic. If the function is periodic, give the period

Name Date Class 14-1 Practice A Graphs of Sine and Cosine Identify whether each function is periodic. If the function is periodic, give the period. 1.. Use f(x) = sinx or g(x) = cosx as a guide. Identify

### Emanuël A. P. Habets, Jacob Benesty, and Patrick A. Naylor. Presented by Amir Kiperwas

Emanuël A. P. Habets, Jacob Benesty, and Patrick A. Naylor Presented by Amir Kiperwas 1 M-element microphone array One desired source One undesired source Ambient noise field Signals: Broadband Mutually

### The Role of High Frequencies in Convolutive Blind Source Separation of Speech Signals

The Role of High Frequencies in Convolutive Blind Source Separation of Speech Signals Maria G. Jafari and Mark D. Plumbley Centre for Digital Music, Queen Mary University of London, UK maria.jafari@elec.qmul.ac.uk,

### THE CITADEL THE MILITARY COLLEGE OF SOUTH CAROLINA. Department of Electrical and Computer Engineering. ELEC 423 Digital Signal Processing

THE CITADEL THE MILITARY COLLEGE OF SOUTH CAROLINA Department of Electrical and Computer Engineering ELEC 423 Digital Signal Processing Project 2 Due date: November 12 th, 2013 I) Introduction In ELEC

### Fault Location Technique for UHV Lines Using Wavelet Transform

International Journal of Electrical Engineering. ISSN 0974-2158 Volume 6, Number 1 (2013), pp. 77-88 International Research Publication House http://www.irphouse.com Fault Location Technique for UHV Lines

### Presentation Outline. Advisors: Dr. In Soo Ahn Dr. Thomas L. Stewart. Team Members: Luke Vercimak Karl Weyeneth. Karl. Luke

Bradley University Department of Electrical and Computer Engineering Senior Capstone Project Presentation May 2nd, 2006 Team Members: Luke Vercimak Karl Weyeneth Advisors: Dr. In Soo Ahn Dr. Thomas L.

### Influences of a Beam-Pipe Discontinuity on the Signals of a Nearby Beam Position Monitor (BPM)

Internal Report DESY M 1-2 May 21 Influences of a Beam-Pipe Discontinuity on the Signals of a Nearby Beam Position Monitor (BPM) A.K. Bandyopadhyay, A. Joestingmeier, A.S. Omar, R. Wanzenberg Deutsches

### ON SAMPLING ISSUES OF A VIRTUALLY ROTATING MIMO ANTENNA. Robert Bains, Ralf Müller

ON SAMPLING ISSUES OF A VIRTUALLY ROTATING MIMO ANTENNA Robert Bains, Ralf Müller Department of Electronics and Telecommunications Norwegian University of Science and Technology 7491 Trondheim, Norway

### LOCAL MULTISCALE FREQUENCY AND BANDWIDTH ESTIMATION. Hans Knutsson Carl-Fredrik Westin Gösta Granlund

LOCAL MULTISCALE FREQUENCY AND BANDWIDTH ESTIMATION Hans Knutsson Carl-Fredri Westin Gösta Granlund Department of Electrical Engineering, Computer Vision Laboratory Linöping University, S-58 83 Linöping,

### Fourier Transform. Any signal can be expressed as a linear combination of a bunch of sine gratings of different frequency Amplitude Phase

Fourier Transform Fourier Transform Any signal can be expressed as a linear combination of a bunch of sine gratings of different frequency Amplitude Phase 2 1 3 3 3 1 sin 3 3 1 3 sin 3 1 sin 5 5 1 3 sin

### Speech Enhancement Based On Spectral Subtraction For Speech Recognition System With Dpcm

International OPEN ACCESS Journal Of Modern Engineering Research (IJMER) Speech Enhancement Based On Spectral Subtraction For Speech Recognition System With Dpcm A.T. Rajamanickam, N.P.Subiramaniyam, A.Balamurugan*,

### Chapter 6: Periodic Functions

Chapter 6: Periodic Functions In the previous chapter, the trigonometric functions were introduced as ratios of sides of a right triangle, and related to points on a circle. We noticed how the x and y

### Microphone Array project in MSR: approach and results

Microphone Array project in MSR: approach and results Ivan Tashev Microsoft Research June 2004 Agenda Microphone Array project Beamformer design algorithm Implementation and hardware designs Demo Motivation

### WARPED FILTER DESIGN FOR THE BODY MODELING AND SOUND SYNTHESIS OF STRING INSTRUMENTS

NORDIC ACOUSTICAL MEETING 12-14 JUNE 1996 HELSINKI WARPED FILTER DESIGN FOR THE BODY MODELING AND SOUND SYNTHESIS OF STRING INSTRUMENTS Helsinki University of Technology Laboratory of Acoustics and Audio

### Proceedings of Meetings on Acoustics

Proceedings of Meetings on Acoustics Volume 19, 2013 http://acousticalsociety.org/ ICA 2013 Montreal Montreal, Canada 2-7 June 2013 Architectural Acoustics Session 2pAAa: Adapting, Enhancing, and Fictionalizing

### Digital Loudspeaker Arrays driven by 1-bit signals

Digital Loudspeaer Arrays driven by 1-bit signals Nicolas Alexander Tatlas and John Mourjopoulos Audiogroup, Electrical Engineering and Computer Engineering Department, University of Patras, Patras, 265

### A Frequency-Invariant Fixed Beamformer for Speech Enhancement

A Frequency-Invariant Fixed Beamformer for Speech Enhancement Rohith Mars, V. G. Reju and Andy W. H. Khong School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore.

Communication Technology Laboratory Wireless Communications Group Prof. Dr. A. Wittneben ETH Zurich, ETF, Sternwartstrasse 7, 8092 Zurich Tel 41 44 632 36 11 Fax 41 44 632 12 09 Lab course Analog Part

### Beamforming in Interference Networks for Uniform Linear Arrays

Beamforming in Interference Networks for Uniform Linear Arrays Rami Mochaourab and Eduard Jorswieck Communications Theory, Communications Laboratory Dresden University of Technology, Dresden, Germany e-mail:

### Multiple Sound Sources Localization Using Energetic Analysis Method

VOL.3, NO.4, DECEMBER 1 Multiple Sound Sources Localization Using Energetic Analysis Method Hasan Khaddour, Jiří Schimmel Department of Telecommunications FEEC, Brno University of Technology Purkyňova

### Transforms and Frequency Filtering

Transforms and Frequency Filtering Khalid Niazi Centre for Image Analysis Swedish University of Agricultural Sciences Uppsala University 2 Reading Instructions Chapter 4: Image Enhancement in the Frequency

### Terminology (1) Chapter 3. Terminology (3) Terminology (2) Transmitter Receiver Medium. Data Transmission. Simplex. Direct link.

Chapter 3 Data Transmission Terminology (1) Transmitter Receiver Medium Guided medium e.g. twisted pair, optical fiber Unguided medium e.g. air, water, vacuum Corneliu Zaharia 2 Corneliu Zaharia Terminology

### ECE 185 ELECTRO-OPTIC MODULATION OF LIGHT

ECE 185 ELECTRO-OPTIC MODULATION OF LIGHT I. Objective: To study the Pockels electro-optic (E-O) effect, and the property of light propagation in anisotropic medium, especially polarization-rotation effects.

### Fiber Optic Communication Systems. Unit-04: Theory of Light. https://sites.google.com/a/faculty.muet.edu.pk/abdullatif

Unit-04: Theory of Light https://sites.google.com/a/faculty.muet.edu.pk/abdullatif Department of Telecommunication, MUET UET Jamshoro 1 Limitations of Ray theory Ray theory describes only the direction

### RECENTLY, there has been an increasing interest in noisy

IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II: EXPRESS BRIEFS, VOL. 52, NO. 9, SEPTEMBER 2005 535 Warped Discrete Cosine Transform-Based Noisy Speech Enhancement Joon-Hyuk Chang, Member, IEEE Abstract In

### Trigonometry. An Overview of Important Topics

Trigonometry An Overview of Important Topics 1 Contents Trigonometry An Overview of Important Topics... 4 UNDERSTAND HOW ANGLES ARE MEASURED... 6 Degrees... 7 Radians... 7 Unit Circle... 9 Practice Problems...

### EXPERIMENTAL EVALUATION OF MODIFIED PHASE TRANSFORM FOR SOUND SOURCE DETECTION

University of Kentucky UKnowledge University of Kentucky Master's Theses Graduate School 2007 EXPERIMENTAL EVALUATION OF MODIFIED PHASE TRANSFORM FOR SOUND SOURCE DETECTION Anand Ramamurthy University

### Mobile Radio Propagation Channel Models

Wireless Information Transmission System Lab. Mobile Radio Propagation Channel Models Institute of Communications Engineering National Sun Yat-sen University Table of Contents Introduction Propagation

### Implementation of a Real-Time Rayleigh, Rician and AWGN Multipath Channel Emulator

Implementation of a Real-Time Rayleigh, Rician and AWGN Multipath Channel Emulator Peter John Green Advanced Communication Department Communication and Network Cluster Institute for Infocomm Research Singapore