Sound Source Localization Using a 2D Acoustic Vector Sensor
|
|
- Trevor York
- 6 years ago
- Views:
Transcription
1 Sound Source Localization Using a 2D Acoustic Vector Sensor Elizabeth Schermerhorn University of Twente P.O. Box 217, 7500AE Enschede The Netherlands e.a.schermerhorn@student.utwente.nl ABSTRACT In this research, a two dimensional acoustic vector sensor is used to determine the direction of arrival (DOA) of a sound source relative to the sensor. Existing solutions have focussed on using two acoustic vector sensors (AVS) or an array of AVS and not on using one AVS. This paper focusses on the accuracy of a sound source localization algorithm and the influence of ambient noise and reflections. Measurements show that as long as the signal-to-noise ratio (SNR) is equal to or larger than 14, the algorithm has a deviation (procentual difference between measured and expected value) of less than 5%. Two experiments have been conducted, one to show that the DOA does not influence the accuracy and a second to show the influence of the SNR on the accuracy. Keywords Acoustic vector sensor (AVS), particle velocity sensor, Signalto-noise Ratio (SNR), Direction of arrival (DOA), Fast fourier transformation (FFT). 1. INTRODUCTION Locating sound sources is a technique which has many applications nowadays. Examples of the use of the sound source localization include localizing people during calamities and the automatic pointing of video cameras to dangerous situations. Commonly used hardware for sound source localization is a microphone array, which is a setup consisting of multiple pressure sensors. Using the localization algorithm Near-field acoustic holography (NAH) [5, 6] the location of the sound source relative to the microphone can be determined. Such a microphone array is based on the difference in arrival time of the acoustic sound wave of the source to the different microphones. This method works but is relatively computational expensive. Furthermore, these arrays are big, especially for low frequency measurements. The particle velocity sensor is one sensor and much smaller. An alternative sensor is an acoustic vector sensor, this sensor measures the particle velocity component of a sound wave in a certain direction. There are two algorithms for an array of particle velocity sensors, namely MUSIC [13] and ESPRIT [1]. There is also an algorithm Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. 23 rd Twente Student Conference on IT June 22 nd, 2015, Enschede, The Netherlands. Copyright 2015, University of Twente, Faculty of Electrical Engineering, Mathematics and Computer Science. Figure 1. Right: Signal with small influence of noise. Left: Signal with great influence of noise. Figure 2. Reflection of sound waves designed for two particle velocity sensors [9]. This algorithm is based on knowing the exact location of the two sensors and measuring the difference in arrival of time. There has not been any research done on the topic using one sensor to locate multiple sources using a 2D acoustic vector sensor (AVS). The influence on the accuracy of the algorithm will be investigated in this paper with respect to the ambient noise and reflections. 1.1 Ambient noise Ambient noise is a collection of all sound waves, which are measured by the sensor but are not the sound waves originating directly from the sound source. Examples of ambient noises are: cars driving by, the sound of air conditioning and the sound of printers. However as there is more ambient noise and the power of the source remains the same, the influence of the ambient noise increases. In Figure 1 two measured signals are shown, both sources have the same power. The left graph is measured in an environment with less ambient noise and the right graph is measured in an environment with more ambient noise. 1.2 Reflection Reflection is the sound that is registered by the AVS that has been bounced off of objects before reaching the AVS. In Figure 2 a visual representation of reflection is shown. 1.3 Use of multiple sources for sound source localization Two sensors In [9] an algorithm is discussed for two, 3 dimensional, 1
2 Figure 3. The particle velocity sensor Figure 5. The longitudinal waves of sound Figure 4. The (x, y, z)-plane AVS placed at a certain distance from one another. By knowing the exact positions of the sensors and calculating the difference in arrival time of the sound at both sensors, using triangulation the exact coordinates can be determined. The advantage of this algorithm and setup is that by using only two sensors it is possible to determine the exact location N Acoustic vector sensors (AVS) An extra extension to the above discussed situation is to take an array of N AVS and place them at arbitrary distances from one another. Where N is usually a number between 6 and 10. There exist two different types of algorithms for the setup, one type can locate a maximum of N-1 sources when N sensors are used. The second makes it possible to locate more than N sources when N sensors are used. Locating N-1 sources. MUSIC[16, 13] and ESPRIT[1] are two algorithms which can maximally determine N-1 sources when N AVS are used. ESPRIT is an improvement of MUSIC on certain aspects and has two large advantages compared to MU- SIC, i.e.,it is computationally less expensive and suffers less from the SNR. Even though ESPRIT has these two advantages, MUSIC tends to be more accurate than ESPRIT [3]. Locating more than N sources. In [17] an algorithm is described for locating more sources than AVS are used. According to [17] The approach is based on the Khatri-Rao product by exploiting the subspace characteristics of the time variant covariance matrices of the uncorrelated quasi-stationary source signals. 1.4 Height of the sources The height influences the determination of the direction of arrival (DOA), here the focus shall lay on locating sound in two dimensions. 2. UNDERLYING PRINCIPLE OF THE AVS In Figure 3 the acoustic vector-sensor used in this research is shown [10]. This sensor measures the particle velocity component of a sound wave. The 2 dimensional plane represented by the sensor can be seen in Figure 4. Since a 2 dimensional sensor is used, the 3 dimensional component is neglected. 2.1 Transformation from particle velocity to input signal In the top part of Figure 5 an acoustic sound wave is shown. This acoustic sound wave consists of two parts, a pressure wave and a particle velocity wave. The denser areas in Figure 5 show the pressure wave. The particle velocity wave is shown in Figure Power of the signal The amplitude of the measured particle velocity wave is dependent on the power of the sound source. When the power of the source increases, the amplitude of the particle velocity wave increases. A higher amplitude, in the long-term, means a more accurate localization. When the amplitude is larger and the ambient noise stays constant, the localization will be more accurate because the ambient noise will have a smaller influence on a stronger signal. 2.3 The sampling frequency The input signal is sampled at equal intervals dt, measured by the audio card MAYA44 and read to a computer. The dt value is dependent on the frequencies that have to be measured. The chosen interval determines the highest frequency possibly to be measured. Since acoustic sound is used in this experiment the frequencies measured should not surpass 22 khz. This value is chosen because the human hearing hearable frequencies range between 20 Hz and 20 khz. This means that the dt interval must be small enough so that this frequency can be measured too. Choosing as sampling frequency means that at most 44100/2=22050 Hertz can be detected, the time interval is set as: Interval = 1/samplingfrequency To make sure that the complete range can be measured the sampling frequency is chosen as instead of to register frequencies which just pass the Hz limit. 2.4 Determining the SNR in db The signal-to-noise ratio is a value which is modeled by: SNR = P s P n,where P s is the power of the signal and P n is the power of the noise, both are in db. The two output signals of the sensor are the sum of all sound waves captured by the sensor, which are registered by the sensor. The higher the SNR is, the lower the influence of the ambient noise. On the other hand, the lower the SNR, the higher the influence of ambient noise. The strength of reflections is dependent on the strength of the sound source, the more power the source has, the higher the reflections become and vice-versa. In Figure 1 two input signals can be seen, on the left two signals with a high SNR and on the right two signals with a low SNR. 2
3 Figure 6. Source in a 3D environment 2.5 The influence of the height of the source with respect to the sensor The sensor is a two dimensional sensor and therefor the elevation will be neglected. The elevation is not of influence on the measurents by this sensor because it is a 2 dimensional sensor. In Figure 6 this principle is shown with the sensor being at (0, 0). 3. HARDWARE RESTRICTIONS There are certain restrictions to the capabilities of the AVS, these are discussed below. 3.1 Determining distance to sources Since only one sensor is being used, it is not possible to calculate the distance to the sources. To calculate the distances triangulation is needed and this cannot be achieved with one sensor, as a minimum two sensors are needed to be able to compute the triangulation. In [9], a localization algorithm is shown with two sensors to locate a source. 3.2 Determine the quadrant Figure 3 illustrates that there are 4 quadrants that can be determined in the same manner in which an x,y coordinate system can be determined. For convenience beginning in the top quadrant and rotating clockwise numbering each quadrant I through IV 4. The sensor however makes no distinction in the direction of arrival when the coordinates are exactly opposite of each other. Meaning that when a source is placed on (-x,-y) in quadrant III the sensor can not determine whether the source is on (x,y) in quadrant I or in quadrant III on (-x,-y). This is due to the fact that the sensor is symmetric and receives exactly the same inputsignals from opposite quadrants. 4. SOUND SOURCE LOCALIZATION AL- GORITHM In Figure 5 an acoustic wave is shown. A particle velocity sensor measures the particle velocity wave and expresses this in sin(v) and cos(v) to model the amplitude. In Figure 8 the determination of the particle velocity from a soundwave is shown. The arrow points in the direction of the sound source and depending on the angle of incidence the amplitudes of the x and y input signals are larger or smaller as can be seen in 8. In Figure 7, a visual representation is shown of the proposed algorithm and its steps. All steps are derived from two input signals. These signals are never altered, but other values are calculated from the input signals. These steps shall be elaborated step by step below. 4.1 Measuring the input signals Two input signals are measured using a MAYA44 USB audio card, one from the x direction and one from the y direction. Every dt seconds a value is taken from both of these signals and placed in an array for respectively the x Figure 7. Representation of the algorithm Figure 8. Determination of the particle velocity 3
4 Figure 10. Left: reach maxima at same moment Right: reach maxima exactly π later Figure 9. An FFT with the calculated threshold values and y values. The dt value is determined by using a sampling frequency of 44.1 khz. The signal is modeled with a constant signal of 1kHz. The wave functions depend on the generated sound wave. 4.2 Determining threshold between sources and noise To determine the threshold which will be used later in the algorithm, the number of peaks (extrema) are calculated from the x input signal. The threshold is used to determine whether a frequency is a relevant acoustic source in the FFT. An FFT of the two input signals is shown in Figure 9. This FFT will be used further on in the algorithm. The black line shows the determined threshold. The amount of peaks of the input signals give a value for the amount of ambient noise and reflections influencing the signal. Because the x and y input signals are correlated (they originate from the same acoustic wave) only the peaks of one of the two signals have to be determined as they will return more or less the same value. The threshold will be determined by the number of peaks which are present, this way the threshold is flexible and adjusts itself to environments with lots of ambient noise and reflections (more peaks) and environments with little ambient noise and reflections (fewer peaks). The threshold is a function of the total power of the signal and decreases when there is more noise while increasing when there is less noise. 4.3 Fourier Transformation to determine sources The fourier transformation [15] takes the input signals of x and y seperately and calculates for both input signals all frequencies with their corresponding amplitudes. In Figure 9 the fourier transformation is shown for two input signals and one acoustic source. The black line represents the threshold which was determined for the two input signals. The large peak which reaches above the threshold is the sole source which was present in that environment. The value of the frequency of this peak will be the value of the frequency of the source. 4.4 Calculating amplitudes of sources The fourier transformation will be used for this step to determine all the peaks above the threshold. In this step, peaks higher than the threshold, will be considered as relevant sources and their angles will be calculated. The frequency along with its amplitude is stored in a matrix for further use. The amplitude is the information needed and the corresponding frequency is kept to remember which amplitude it corresponds to. 4.5 Determining DOA All amplitudes higher than the threshold in the FFT are likely to be a source. For the frequencies corresponding to these amplitudes, the DOA will be determined with the following equation: angle = arctan(amp y/amp x) The angle relative to the x-axis in the positive quadrant is being determined. The amp y and amp x are respectively the amplitudes of y and x at the same frequency. 4.6 Determining quadrant In Section 3.2, the issue has been discussed with determinig the quadrant. In Figure 10, two possibilities are shown, left is when both input signals reach their maxima at the same time. While right shows when they have a phase shift of exactly 180 degrees. This principle difference makes it possible to determine the quadrant. The method of determining which of the two applies to a certain situation is done by multiplying the two input signals. For example, take two input signals x = sin(t) and y = sin(t), where t is time and x and y are the input signals. In this example both functions are positive (or negative) which generate the following function: x y = sin(t) 2. This generates a positive function moving it to the positive quadrant and oscillating around a positive integer instead of 0. On the other hand, when x or y (not both or neither) represents a negative function, then the multiplication would become x y = sin(t) 2. This moves the result to the negative quadrant giving other information so that the quadrant of arrival can be determined. When x y is positive the source is in quadrant I or III, when x y are negative the source is in quadrant II or IV. In Figure 4 the quadrants are drawn. 4.7 Drawing direction of arrival At last the source can be plotted in a graph giving the calculated angle and the quadrant which it was determined in. 5. ACCURACY DEPENDENT VARIABLES The accuracy of the algorithm is dependent on certain properties and their values. The two properties among which the accuracy depends on are The ambient noise and reflections The soundintensity 5.1 Ambient noise and reflections These are two aspects of the environment in which the algorithm will be tested. When the ambient noise and reflections are larger, the signal will suffer more from non 4
5 related information. This makes the localization less accurate when there is more ambient noise and more reflections. 5.2 Power of the source The higher the power of the source, the more accurate the localization when the noise remains constant. This is because the influence of ambient noise and reflections becomes less as the soundintensity increases. 6. SIMULATIONS Before the actual tests are run, simulations have been designed and tested to evaluate the performance of the algorithm. 6.1 Simulation input signal Since the amplitudes of the input signals are dependent on the angle of incidence and frequency of the sound source, the simulated functions must contain the angle of incidence and the frequency. This translates in: X = sin(angle π/180) sqrt(2) sin(2 π f t) Figure 11. Histogram of the calculated angles with various SNR(1,6,11) Y = cos(angle π/180) sqrt(2) sin(2 π f t) X and Y model input signals with a certain frequency f and an angle angle. Since the function is in radials the angle in degrees is transformed by multiplying with 180/π. The squareroot of two is taken to give the signal a power of 0dBW. The signal is modeled in this matter to make the addition of noise easier. These are two perfect input signals without ambient noise and reflections. Noise can be added to the X and Y input signals with the following function: X = awgn(x, SNR, 0) Y = awgn(y, SNR, 0) Noise is added by using a predefined function in matlab called awgn() which stands for add white gaussian noise. By changing the value of the SNR, more or less noise is added to the signal since the power of the signal always remains the same. The effect of adding noise, is that the signal gets distorted just like the real signal shall be. 6.2 Accuracy tests Two simulations have been conducted, the first simulation calculates the angle 100 times from the same input value for three different Signal-to-noise ratios. This gives different output values since the function awgn() and the fourier transformation are not deterministic. The second test calculates 100 times the angle with a SNR of 5 for each angle bewteen zero and 90 degrees Accuracy test dependent on SNR For this test the same angle (of 16 degrees) has been determined for three different values of the SNR. The three values used for the SNR are 1, 6 and 11. The following test has been conducted: 1. Calculating the same angle from the same input values 100 times. 2. Changing the signal-to-noise ratio(snr) for the next 100 calculations. In Figure 11 a histogram is shown. This histogram shows the 100 calculated values for each different SNR. From this histogram it is clear that the accuracy decreases when the SNR decreases. The bars of the histogram have been taken out for visual purposes. Figure 12. The calculated angles during the simulation and their occurance Accuracy per angle In Figure 12 a plot is shown with different angles which have been calculated 100 times with the same input (frequency, angle and SNR). The graph shows how often a certain angle is estimated and plotted. From left to right, the angles which should have been calculated are 5+k 10 and k [1, 8]. This figure shows that the accuracy does not depend on the angle. When the simulatoin is run multiple times, the results are different for each round. The DOA which has been calculated correctly most frequently changes with every new test. 7. EXPERIMENTS WITH AVS To test the algorithm in a real physical environment, two experiments have been conducted, one to determine the effects of the ambient noise on the signal and a second to measure the accuracy at certain angles. 7.1 setup The setup which has been used for the experiments can be seen in Figure Sensor 5
6 Figure 13. The experimental setup Figure 15. The accuracy of the algorithm as function of the SNR testing, there was a constant ambient noise of 46 db. For all values of steps of 10 from 50 db to 100 db have been measured and the deviation has been determined. For a period of 12 seconds the input signals were measured for all different SNRs. After this the algorithm has calculated the estimated angles of the different input signals. The deviation is a value which is computed with the following function: deviation = (angle1 angle2 )/angle2 Figure 14. The AVS used during the tests Here angle1 is the angle which the sensor has relative to the source, angle2 is the computed angle by the algorithm. The signal-to-noise ratio was calculated by the following function: In Figure 13 the sensor can be seen in the upper left corner. This is the position of the AVS in the setup. The sensor does not move during the tests to keep the data accurate and consistent. The sensor used during this experiment is a particle velocity sensor which is shown in Figure 14. The particle velocity sensor in Figure 3 is in fact the small dot in the middle of the smaller square in Figure SN R = Ps Pr Ps is the sound intensity in db measured by the decibel meter and pr is the premeasured sound intensity of 46 db. This value was determined (using a decibel meter) before the testing started and is specific for this test and this environment. In Table 1 the determined values are shown. Here the angle at which the sensor was placed and which should have been calculated is rad (45 degrees). It was impossible to determine the angle for the first input of the SNR of 50-46=4, since there was no significant peak in the Fourier transformation. Sound source The sound source is a speaker with a frquency range of 20Hz to 22kHz. The speaker is used to generate single tones for the measurements. All tests are conducted with a frequency set at 1 khz and a varying amplitude. The sound intensity at the position of the sensor was measured using an acoustic sound db-meter Table 1. SNR and the influence on the deviation SNR calculated angle(rad) percentage deviation = = % = % = % = % = % Turnpad The turnpad turns the sensor so that different angles can be measured. Turning the sensor is the same as turning the source around the sensor. The turnpad is controlled by a computerprogram with a resolution of 2.5 degrees Low frequency filter Due to a great influence of the air conditioning in the test area the choice has been made to filter the low frequencies (below 180 Hz). By filtering these lower frequencies measuring was easier. The filter uses a first order highpass filter with a cut-off frequency of 180Hz to filter these disturbances. 7.3 Determining the accuracy per angle Algorithm accuracy with different SNRs In this experiment the angles have been determined to test wether the angle influences the accuracy. In Tabel 2 the results are shown with the expected angle, the calculated angle and the deviation between waht is expected and what is determined. The first experiment relates to determining the accuracy of the algorithm with different signal-to-noise ratios. During In Figure 16 the rotation used during the test is shown. First the source is in the first quadrant at 1/4π. Then the 7.2 6
7 Table 2. The calculated accuracy per angle(degrees) Expected calculated deviation 1.06% 5.6% 4.7% 3.11% 1.9% Expected calculated deviation 0.78% 0.8% 3.2% 1.6% 3.9% 3.2% Figure 16. The rotation during the second test through two quadrants source is moved in steps to the x-axis, or 1/2π. Here the source crosses over into the second quadrant continuing to move until it reaches 1/4π. In Table 2 the top part shows the movement from 1/4π till a 1/2π, and the bottom part from 1/2π in the second quadrant to 1/4π. The same measurments have been conducted with the a SNR of 40. This experiment shows that it does not matter which angle is measured, the angle has no influence on the accuracy of the algorithm. 8. DISCUSSION During the first simulation the conclusion was made that the greater the ambient noise, the less accurate the localization results are. This has been proven again by performing experiments in a realistic environment. Figure 15 shows the deviation in relation to different SNRs. The second simulation and experiment show that the angle does not influence the calculation of the angle. Due to the fact that a vector can be split into two components it would be expected that when one of these components becomes tiny (0 or 90 degrees), the accuracy would decrease. However this is not the case and the deviation lies between 1% and 3%. The deviation in accuracy could have resulted from: 1. The ambient noise increased for a short time 2. The set position was actually off by one or two degrees Since the deviations are so small these two are likely to have happened. The fact that it is possible to locate multiple sources using one sensor is a great advantage over previous methods of using multiple AVS to locate more sources. 9. CONCLUSION AND FUTURE WORK An algorithm has been designed and implemented in Matlab to locate acoustic sources. With simulations primary tests have been run to test the algorithm. Furthermore two experiments have been conducted, the first test was concerned with the influence of the SNR on the accuracy of the algorithm. Measurements were performed with different sound intensity levels.for a signal of 15 db the algorithm can determine the angle of incidence with a 5% accuracy. Whereas for 25 db the accuracy increases to 1%. The second test investogated if there was a difference in accuracy at various angles of incidence. This test has shown that between 0 and 180 degrees the angle does not influence the accuracy of the algorithm. The measurements show that the deviation stays below the 5%. Future work includes an extension of this algorithm. For instances, A second sensor can also be added, adding the second sensor the exact location of the source can be determined. When adding a second sensor, the same algorithm is used and theadditionally, a pressure sensor may be added to determine which quadrant the acoustic source originated from. 10. REFERENCES [1] J. Bermudez, R.C. Chin, P. Davoodian, A.T.Y. Lok, Z. Aliyazicioglu, H.K. Wang. Simulation study on DOA estimation using ESPRIT algorithm. WCECS 10/2009, San Francisco, USA. [2] H.E. de Bree and W.F. Druyvesteyn. An acoustic vector sensor based method to measure the bearing, elevation and range of a single dominant source as well as the ground impedance. Euronoise 2009, Edinburgh. [3] N.P. Waweru, D.B.O. Konditi, P.K. Langat Perfromance analysis of MUSIC, Root-MUSIC and ESPRIT DOA Estimation Algorithm. International Journal of Electrical, COmputer,Electronics and Communication Engineering Vol:9, No:1, [4] J.T. Fricke, H.E. de Bree, A. Siegel. Source localization with acoustic vector sensors. Ilmenau University of Technology, Internationales Wissenschaftliches Kolloquium september [5] E.F. Grande. Near-field acoustic holography with sound pressure and particle velocity measurements. PhD thesis, Technical University of Denmark, June [6] D. Hu, C. Bi, Y. Zhang and L. Geng. Extension of planar nearfield acoustic holography for sound source identification in a noisy environment. Journal of Sound and Vibration, [7] W. Jing, D. Fernandez Comesana and D. Perez Cabo. Sound source localisation using a single acoustic vector sensor and multichannel microphone phased array. Internoise 2014 [8] J.P. Kitchens Acoustic Vector-Sensor Array Performance, Chapter 2. Massachusetts Institute of Technology, June [9] E. Nixon, S. Sen, M. Akcakaya, E. Richter and A. Nehori. 3D source localization using acoustic vector sensor arrays. Washington University St. Louis, Department of Electrical Systems Engineering, [10] O. Pjetri, R.J. Wiegerink, T.S.J. Lammerink, G.J.M. Krijnen. A novel two dimensional particle velocity sensor. International Congress on Acoustics, jun 2013, Montreal, Canada, ISSN X [11] R. Raangs. Exploring the use of the Microflown, Chapter 5. Dutch Technology Foundation, ISBN , [12] Y. Song, K.T. Wong and Y. Li. Direction finding using a biaxial particle-velocity sensor. Journal of Sound and Vibration, 15 October [13] H. Tang. DOA estimation based on MUSIC algorithm. 2014/5/16 Linneuniversitetet Kalmar Vaxjo. [14] R. Visser. Inverse Source Identification based on Acoustic Particle Velocity Measurements. internoise 7
8 2012. [15] E.A. Williams. Fourier Acoustics: Sound Radiation and Nearfield Acoustical Holography, Chapter , ISBN-13: [16] J.W. Wind, E. Tijs and H.E. de Bree. Source localization using Acoustic Vector-Sensors: A music approach. Novem 2009, Oxford. [17] S. Zhao, D.L. Jones. Underdetermine 2D DOA estimation using acoustic vector sensor. Advanced Digital Sciences Center from SingaporeŠs Agency for Science, Technology and Research Advanced digital sciences center from Singapore s Agency for science, technology and research,
Scan&Paint, a new fast tool for sound source localization and quantification of machinery in reverberant conditions
Scan&Paint, a new fast tool for sound source localization and quantification of machinery in reverberant conditions Dr. Hans-Elias de Bree, Mr. Andrea Grosso, Dr. Jelmer Wind, Ing. Emiel Tijs, Microflown
More informationLocalization 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
More informationMultiple 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
More informationRobust 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
More informationLaboratory Assignment 2 Signal Sampling, Manipulation, and Playback
Laboratory Assignment 2 Signal Sampling, Manipulation, and Playback PURPOSE This lab will introduce you to the laboratory equipment and the software that allows you to link your computer to the hardware.
More informationA. Czyżewski, J. Kotus Automatic localization and continuous tracking of mobile sound sources using passive acoustic radar
A. Czyżewski, J. Kotus Automatic localization and continuous tracking of mobile sound sources using passive acoustic radar Multimedia Systems Department, Gdansk University of Technology, Narutowicza 11/12,
More informationinter.noise 2000 The 29th International Congress and Exhibition on Noise Control Engineering August 2000, Nice, FRANCE
Copyright SFA - InterNoise 2000 1 inter.noise 2000 The 29th International Congress and Exhibition on Noise Control Engineering 27-30 August 2000, Nice, FRANCE I-INCE Classification: 7.2 MICROPHONE T-ARRAY
More informationAcoustic Resonance Lab
Acoustic Resonance Lab 1 Introduction This activity introduces several concepts that are fundamental to understanding how sound is produced in musical instruments. We ll be measuring audio produced from
More informationPerformance Analysis of a 1-bit Feedback Beamforming Algorithm
Performance Analysis of a 1-bit Feedback Beamforming Algorithm Sherman Ng Mark Johnson Electrical Engineering and Computer Sciences University of California at Berkeley Technical Report No. UCB/EECS-2009-161
More informationDiscrete Fourier Transform
6 The Discrete Fourier Transform Lab Objective: The analysis of periodic functions has many applications in pure and applied mathematics, especially in settings dealing with sound waves. The Fourier transform
More informationMultiple Input Multiple Output (MIMO) Operation Principles
Afriyie Abraham Kwabena Multiple Input Multiple Output (MIMO) Operation Principles Helsinki Metropolia University of Applied Sciences Bachlor of Engineering Information Technology Thesis June 0 Abstract
More informationHaptic control in a virtual environment
Haptic control in a virtual environment Gerard de Ruig (0555781) Lourens Visscher (0554498) Lydia van Well (0566644) September 10, 2010 Introduction With modern technological advancements it is entirely
More informationNonuniform multi level crossing for signal reconstruction
6 Nonuniform multi level crossing for signal reconstruction 6.1 Introduction In recent years, there has been considerable interest in level crossing algorithms for sampling continuous time signals. Driven
More informationWhat applications is a cardioid subwoofer configuration appropriate for?
SETTING UP A CARDIOID SUBWOOFER SYSTEM Joan La Roda DAS Audio, Engineering Department. Introduction In general, we say that a speaker, or a group of speakers, radiates with a cardioid pattern when it radiates
More informationarxiv: 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
More informationAnalysis on Acoustic Attenuation by Periodic Array Structure EH KWEE DOE 1, WIN PA PA MYO 2
www.semargroup.org, www.ijsetr.com ISSN 2319-8885 Vol.03,Issue.24 September-2014, Pages:4885-4889 Analysis on Acoustic Attenuation by Periodic Array Structure EH KWEE DOE 1, WIN PA PA MYO 2 1 Dept of Mechanical
More informationChapter 2. Meeting 2, Measures and Visualizations of Sounds and Signals
Chapter 2. Meeting 2, Measures and Visualizations of Sounds and Signals 2.1. Announcements Be sure to completely read the syllabus Recording opportunities for small ensembles Due Wednesday, 15 February:
More informationScan-based near-field acoustical holography on rocket noise
Scan-based near-field acoustical holography on rocket noise Michael D. Gardner N283 ESC Provo, UT 84602 Scan-based near-field acoustical holography (NAH) shows promise in characterizing rocket noise source
More informationTHE PATTERNS OF THE SOUND INTENSITY DISTRIBUTION OF MIDRANGE LOUDSPEAKER
Proceeding of International Conference On Research, Implementation And Education Of Mathematics And Sciences 2014, Yogyakarta State University, 18-20 May 2014 THE PATTERNS OF THE SOUND INTENSITY DISTRIBUTION
More informationTHE SINUSOIDAL WAVEFORM
Chapter 11 THE SINUSOIDAL WAVEFORM The sinusoidal waveform or sine wave is the fundamental type of alternating current (ac) and alternating voltage. It is also referred to as a sinusoidal wave or, simply,
More informationLONG RANGE SOUND SOURCE LOCALIZATION EXPERIMENTS
LONG RANGE SOUND SOURCE LOCALIZATION EXPERIMENTS Flaviu Ilie BOB Faculty of Electronics, Telecommunications and Information Technology Technical University of Cluj-Napoca 26-28 George Bariţiu Street, 400027
More informationModal Parameter Identification of A Continuous Beam Bridge by Using Grouped Response Measurements
Modal Parameter Identification of A Continuous Beam Bridge by Using Grouped Response Measurements Hasan CEYLAN and Gürsoy TURAN 2 Research and Teaching Assistant, Izmir Institute of Technology, Izmir,
More informationInvestigating Electromagnetic and Acoustic Properties of Loudspeakers Using Phase Sensitive Equipment
Investigating Electromagnetic and Acoustic Properties of Loudspeakers Using Phase Sensitive Equipment Katherine Butler Department of Physics, DePaul University ABSTRACT The goal of this project was to
More informationinter.noise 2000 The 29th International Congress and Exhibition on Noise Control Engineering August 2000, Nice, FRANCE
Copyright SFA - InterNoise 2000 1 inter.noise 2000 The 29th International Congress and Exhibition on Noise Control Engineering 27-30 August 2000, Nice, FRANCE I-INCE Classification: 7.2 MICROPHONE ARRAY
More informationSpectrum Analysis: The FFT Display
Spectrum Analysis: The FFT Display Equipment: Capstone, voltage sensor 1 Introduction It is often useful to represent a function by a series expansion, such as a Taylor series. There are other series representations
More information648. Measurement of trajectories of piezoelectric actuators with laser Doppler vibrometer
648. Measurement of trajectories of piezoelectric actuators with laser Doppler vibrometer V. Grigaliūnas, G. Balčiūnas, A.Vilkauskas Kaunas University of Technology, Kaunas, Lithuania E-mail: valdas.grigaliunas@ktu.lt
More informationThe Discrete Fourier Transform. Claudia Feregrino-Uribe, Alicia Morales-Reyes Original material: Dr. René Cumplido
The Discrete Fourier Transform Claudia Feregrino-Uribe, Alicia Morales-Reyes Original material: Dr. René Cumplido CCC-INAOE Autumn 2015 The Discrete Fourier Transform Fourier analysis is a family of mathematical
More informationDifferent 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
More informationSmart antenna for doa using music and esprit
IOSR Journal of Electronics and Communication Engineering (IOSRJECE) ISSN : 2278-2834 Volume 1, Issue 1 (May-June 2012), PP 12-17 Smart antenna for doa using music and esprit SURAYA MUBEEN 1, DR.A.M.PRASAD
More informationRoom 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
More informationTHE USE OF VOLUME VELOCITY SOURCE IN TRANSFER MEASUREMENTS
THE USE OF VOLUME VELOITY SOURE IN TRANSFER MEASUREMENTS N. Møller, S. Gade and J. Hald Brüel & Kjær Sound and Vibration Measurements A/S DK850 Nærum, Denmark nbmoller@bksv.com Abstract In the automotive
More informationActive noise control at a moving virtual microphone using the SOTDF moving virtual sensing method
Proceedings of ACOUSTICS 29 23 25 November 29, Adelaide, Australia Active noise control at a moving rophone using the SOTDF moving sensing method Danielle J. Moreau, Ben S. Cazzolato and Anthony C. Zander
More informationCHAPTER. delta-sigma modulators 1.0
CHAPTER 1 CHAPTER Conventional delta-sigma modulators 1.0 This Chapter presents the traditional first- and second-order DSM. The main sources for non-ideal operation are described together with some commonly
More informationUNIT 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
More informationSound source localization accuracy of ambisonic microphone in anechoic conditions
Sound source localization accuracy of ambisonic microphone in anechoic conditions Pawel MALECKI 1 ; 1 AGH University of Science and Technology in Krakow, Poland ABSTRACT The paper presents results of determination
More informationLow frequency sound reproduction in irregular rooms using CABS (Control Acoustic Bass System) Celestinos, Adrian; Nielsen, Sofus Birkedal
Aalborg Universitet Low frequency sound reproduction in irregular rooms using CABS (Control Acoustic Bass System) Celestinos, Adrian; Nielsen, Sofus Birkedal Published in: Acustica United with Acta Acustica
More informationNew developments in near-field acoustic holography
Please leave this heading unchanged! New developments in near-field acoustic holography N.B. Roozen*, A.C. Geerlings, B.T. Verhaar, T. Vliegenthart. Philips Applied Technologies, High Tech Campus 7, 5656
More informationTIMA Lab. Research Reports
ISSN 292-862 TIMA Lab. Research Reports TIMA Laboratory, 46 avenue Félix Viallet, 38 Grenoble France ON-CHIP TESTING OF LINEAR TIME INVARIANT SYSTEMS USING MAXIMUM-LENGTH SEQUENCES Libor Rufer, Emmanuel
More informationMASSACHUSETTS INSTITUTE OF TECHNOLOGY /6.071 Introduction to Electronics, Signals and Measurement Spring 2006
MASSACHUSETTS INSTITUTE OF TECHNOLOGY.071/6.071 Introduction to Electronics, Signals and Measurement Spring 006 Lab. Introduction to signals. Goals for this Lab: Further explore the lab hardware. The oscilloscope
More informationStudy of Standing Waves to Find Speed of Sound in Air
Study of Standing Waves to Find Speed of Sound in Air Purpose Using mobile devices as sound analyzer and sound generator to study standing waves and determine the speed of sound in air. Theory The velocity
More informationAudio Engineering Society Convention Paper Presented at the 110th Convention 2001 May Amsterdam, The Netherlands
Audio Engineering Society Convention Paper Presented at the th Convention May 5 Amsterdam, The Netherlands This convention paper has been reproduced from the author's advance manuscript, without editing,
More informationPHYS102 Previous Exam Problems. Sound Waves. If the speed of sound in air is not given in the problem, take it as 343 m/s.
PHYS102 Previous Exam Problems CHAPTER 17 Sound Waves Sound waves Interference of sound waves Intensity & level Resonance in tubes Doppler effect If the speed of sound in air is not given in the problem,
More informationAcoustics and Fourier Transform Physics Advanced Physics Lab - Summer 2018 Don Heiman, Northeastern University, 1/12/2018
1 Acoustics and Fourier Transform Physics 3600 - Advanced Physics Lab - Summer 2018 Don Heiman, Northeastern University, 1/12/2018 I. INTRODUCTION Time is fundamental in our everyday life in the 4-dimensional
More informationAntennas and Propagation. Chapter 5c: Array Signal Processing and Parametric Estimation Techniques
Antennas and Propagation : Array Signal Processing and Parametric Estimation Techniques Introduction Time-domain Signal Processing Fourier spectral analysis Identify important frequency-content of signal
More informationSpeech Enhancement in Presence of Noise using Spectral Subtraction and Wiener Filter
Speech Enhancement in Presence of Noise using Spectral Subtraction and Wiener Filter 1 Gupteswar Sahu, 2 D. Arun Kumar, 3 M. Bala Krishna and 4 Jami Venkata Suman Assistant Professor, Department of ECE,
More informationWIND 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
More informationOrthonormal bases and tilings of the time-frequency plane for music processing Juan M. Vuletich *
Orthonormal bases and tilings of the time-frequency plane for music processing Juan M. Vuletich * Dept. of Computer Science, University of Buenos Aires, Argentina ABSTRACT Conventional techniques for signal
More informationProperties of Sound. Goals and Introduction
Properties of Sound Goals and Introduction Traveling waves can be split into two broad categories based on the direction the oscillations occur compared to the direction of the wave s velocity. Waves where
More information8.3 Basic Parameters for Audio
8.3 Basic Parameters for Audio Analysis Physical audio signal: simple one-dimensional amplitude = loudness frequency = pitch Psycho-acoustic features: complex A real-life tone arises from a complex superposition
More informationStructure 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
More informationFrequency Domain Representation of Signals
Frequency Domain Representation of Signals The Discrete Fourier Transform (DFT) of a sampled time domain waveform x n x 0, x 1,..., x 1 is a set of Fourier Coefficients whose samples are 1 n0 X k X0, X
More informationDirection-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,
More informationECMA-108. Measurement of Highfrequency. emitted by Information Technology and Telecommunications Equipment. 4 th Edition / December 2008
ECMA-108 4 th Edition / December 2008 Measurement of Highfrequency Noise emitted by Information Technology and Telecommunications Equipment COPYRIGHT PROTECTED DOCUMENT Ecma International 2008 Standard
More informationDISTANCE CODING AND PERFORMANCE OF THE MARK 5 AND ST350 SOUNDFIELD MICROPHONES AND THEIR SUITABILITY FOR AMBISONIC REPRODUCTION
DISTANCE CODING AND PERFORMANCE OF THE MARK 5 AND ST350 SOUNDFIELD MICROPHONES AND THEIR SUITABILITY FOR AMBISONIC REPRODUCTION T Spenceley B Wiggins University of Derby, Derby, UK University of Derby,
More informationInfluence of the Vibrational Properties of the Resonance Board on the Acoustical Quality of a Piano
Influence of the Vibrational Properties of the Resonance Board on the Acoustical Quality of a Piano Zhenbo Liu,* Yixing Liu, and Jun Shen The vibrational properties of eight resonance boards made from
More informationIntroduction to Telecommunications and Computer Engineering Unit 3: Communications Systems & Signals
Introduction to Telecommunications and Computer Engineering Unit 3: Communications Systems & Signals Syedur Rahman Lecturer, CSE Department North South University syedur.rahman@wolfson.oxon.org Acknowledgements
More informationImproving room acoustics at low frequencies with multiple loudspeakers and time based room correction
Improving room acoustics at low frequencies with multiple loudspeakers and time based room correction S.B. Nielsen a and A. Celestinos b a Aalborg University, Fredrik Bajers Vej 7 B, 9220 Aalborg Ø, Denmark
More informationVoice Activity Detection
Voice Activity Detection Speech Processing Tom Bäckström Aalto University October 2015 Introduction Voice activity detection (VAD) (or speech activity detection, or speech detection) refers to a class
More informationSound is the human ear s perceived effect of pressure changes in the ambient air. Sound can be modeled as a function of time.
2. Physical sound 2.1 What is sound? Sound is the human ear s perceived effect of pressure changes in the ambient air. Sound can be modeled as a function of time. Figure 2.1: A 0.56-second audio clip of
More information3D Intermodulation Distortion Measurement AN 8
3D Intermodulation Distortion Measurement AN 8 Application Note to the R&D SYSTEM The modulation of a high frequency tone f (voice tone and a low frequency tone f (bass tone is measured by using the 3D
More informationLab S-1: Complex Exponentials Source Localization
DSP First, 2e Signal Processing First Lab S-1: Complex Exponentials Source Localization Pre-Lab: Read the Pre-Lab and do all the exercises in the Pre-Lab section prior to attending lab. Verification: The
More informationAcoustic Signature of an Unmanned Air Vehicle - Exploitation for Aircraft Localisation and Parameter Estimation
Acoustic Signature of an Unmanned Air Vehicle - Exploitation for Aircraft Localisation and Parameter Estimation S. Sadasivan, M. Gurubasavaraj and S. Ravi Sekar Aeronautical Development Establishment,
More informationONE of the most common and robust beamforming algorithms
TECHNICAL NOTE 1 Beamforming algorithms - beamformers Jørgen Grythe, Norsonic AS, Oslo, Norway Abstract Beamforming is the name given to a wide variety of array processing algorithms that focus or steer
More informationName: Lab Partner: Section:
Chapter 11 Wave Phenomena Name: Lab Partner: Section: 11.1 Purpose Wave phenomena using sound waves will be explored in this experiment. Standing waves and beats will be examined. The speed of sound will
More informationProceedings of Meetings on Acoustics
Proceedings of Meetings on Acoustics Volume 19, 2013 http://acousticalsociety.org/ ICA 2013 Montreal Montreal, Canada 2-7 June 2013 Structural Acoustics and Vibration Session 5aSA: Applications in Structural
More informationMusic 171: Sinusoids. Tamara Smyth, Department of Music, University of California, San Diego (UCSD) January 10, 2019
Music 7: Sinusoids Tamara Smyth, trsmyth@ucsd.edu Department of Music, University of California, San Diego (UCSD) January 0, 209 What is Sound? The word sound is used to describe both:. an auditory sensation
More informationChapter 5. Signal Analysis. 5.1 Denoising fiber optic sensor signal
Chapter 5 Signal Analysis 5.1 Denoising fiber optic sensor signal We first perform wavelet-based denoising on fiber optic sensor signals. Examine the fiber optic signal data (see Appendix B). Across all
More informationPRACTICAL ASPECTS OF ACOUSTIC EMISSION SOURCE LOCATION BY A WAVELET TRANSFORM
PRACTICAL ASPECTS OF ACOUSTIC EMISSION SOURCE LOCATION BY A WAVELET TRANSFORM Abstract M. A. HAMSTAD 1,2, K. S. DOWNS 3 and A. O GALLAGHER 1 1 National Institute of Standards and Technology, Materials
More informationImage analysis. CS/CME/BIOPHYS/BMI 279 Fall 2015 Ron Dror
Image analysis CS/CME/BIOPHYS/BMI 279 Fall 2015 Ron Dror A two- dimensional image can be described as a function of two variables f(x,y). For a grayscale image, the value of f(x,y) specifies the brightness
More informationMultiple sound sources localization in free field using acoustic vector sensor
Multimed Tools Appl (2015) 74:4235 4251 DOI 10.1007/s11042-013-1549-y Multiple sound sources localization in free field using acoustic vector sensor Józef Kotus Published online: 21 June 2013 # The Author(s)
More informationCauses for Amplitude Compression AN 12
Causes for Amplitude AN 2 Application Note to the R&D SYSTEM Both thermal and nonlinear effects limit the amplitude of the fundamental component in the state variables and in the sound pressure output.
More informationLane Detection in Automotive
Lane Detection in Automotive Contents Introduction... 2 Image Processing... 2 Reading an image... 3 RGB to Gray... 3 Mean and Gaussian filtering... 5 Defining our Region of Interest... 6 BirdsEyeView Transformation...
More informationApplying the Filtered Back-Projection Method to Extract Signal at Specific Position
Applying the Filtered Back-Projection Method to Extract Signal at Specific Position 1 Chia-Ming Chang and Chun-Hao Peng Department of Computer Science and Engineering, Tatung University, Taipei, Taiwan
More informationADAPTIVE ANTENNAS. TYPES OF BEAMFORMING
ADAPTIVE ANTENNAS TYPES OF BEAMFORMING 1 1- Outlines This chapter will introduce : Essential terminologies for beamforming; BF Demonstrating the function of the complex weights and how the phase and amplitude
More informationFundamentals of Digital Audio *
Digital Media The material in this handout is excerpted from Digital Media Curriculum Primer a work written by Dr. Yue-Ling Wong (ylwong@wfu.edu), Department of Computer Science and Department of Art,
More informationWeek I AUDL Signals & Systems for Speech & Hearing. Sound is a SIGNAL. You may find this course demanding! How to get through it: What is sound?
AUDL Signals & Systems for Speech & Hearing Week I You may find this course demanding! How to get through it: Consult the Web site: www.phon.ucl.ac.uk/courses/spsci/sigsys Essential to do the reading and
More informationVibration Analysis on Rotating Shaft using MATLAB
IJSTE - International Journal of Science Technology & Engineering Volume 3 Issue 06 December 2016 ISSN (online): 2349-784X Vibration Analysis on Rotating Shaft using MATLAB K. Gopinath S. Periyasamy PG
More informationGet Rhythm. Semesterthesis. Roland Wirz. Distributed Computing Group Computer Engineering and Networks Laboratory ETH Zürich
Distributed Computing Get Rhythm Semesterthesis Roland Wirz wirzro@ethz.ch Distributed Computing Group Computer Engineering and Networks Laboratory ETH Zürich Supervisors: Philipp Brandes, Pascal Bissig
More informationSTATION NUMBER: LAB SECTION: Filters. LAB 6: Filters ELECTRICAL ENGINEERING 43/100 INTRODUCTION TO MICROELECTRONIC CIRCUITS
Lab 6: Filters YOUR EE43/100 NAME: Spring 2013 YOUR PARTNER S NAME: YOUR SID: YOUR PARTNER S SID: STATION NUMBER: LAB SECTION: Filters LAB 6: Filters Pre- Lab GSI Sign- Off: Pre- Lab: /40 Lab: /60 Total:
More informationE40M Sound and Music. M. Horowitz, J. Plummer, R. Howe 1
E40M Sound and Music M. Horowitz, J. Plummer, R. Howe 1 LED Cube Project #3 In the next several lectures, we ll study Concepts Coding Light Sound Transforms/equalizers Devices LEDs Analog to digital converters
More informationEnhancement of Speech Signal by Adaptation of Scales and Thresholds of Bionic Wavelet Transform Coefficients
ISSN (Print) : 232 3765 An ISO 3297: 27 Certified Organization Vol. 3, Special Issue 3, April 214 Paiyanoor-63 14, Tamil Nadu, India Enhancement of Speech Signal by Adaptation of Scales and Thresholds
More informationApproaches 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:
More informationSimulating a PTA with metronomes and microphones: A user s guide for a double-metronome timing & correlation demonstration
Simulating a PTA with metronomes and microphones: A user s guide for a double-metronome timing & correlation demonstration October 21, 2015 Page 1 Contents I Purpose....................................................
More informationMeasurement System for Acoustic Absorption Using the Cepstrum Technique. Abstract. 1. Introduction
The 00 International Congress and Exposition on Noise Control Engineering Dearborn, MI, USA. August 9-, 00 Measurement System for Acoustic Absorption Using the Cepstrum Technique E.R. Green Roush Industries
More informationElectronic Noise Effects on Fundamental Lamb-Mode Acoustic Emission Signal Arrival Times Determined Using Wavelet Transform Results
DGZfP-Proceedings BB 9-CD Lecture 62 EWGAE 24 Electronic Noise Effects on Fundamental Lamb-Mode Acoustic Emission Signal Arrival Times Determined Using Wavelet Transform Results Marvin A. Hamstad University
More informationFourier and Wavelets
Fourier and Wavelets Why do we need a Transform? Fourier Transform and the short term Fourier (STFT) Heisenberg Uncertainty Principle The continues Wavelet Transform Discrete Wavelet Transform Wavelets
More informationNear field phased array DOA and range estimation of UHF RFID tags
Near field phased array DOA and range estimation of UHF RFID tags Jordy Huiting, André B.J. Kokkeler and Gerard J.M. Smit Dep. of Electrical Engineering, Mathematics and Computer Sciencem, University of
More informationImplementation of Digital Signal Processing: Some Background on GFSK Modulation
Implementation of Digital Signal Processing: Some Background on GFSK Modulation Sabih H. Gerez University of Twente, Department of Electrical Engineering s.h.gerez@utwente.nl Version 5 (March 9, 2016)
More informationAcoustic Calibration Service in Automobile Field at NIM, China
Acoustic Calibration Service in Automobile Field at NIM, China ZHONG Bo National Institute of Metrology, China zhongbo@nim.ac.cn Contents 1 Overview of Calibration Services 2 Anechoic Room Calibration
More informationIOMAC' May Guimarães - Portugal
IOMAC'13 5 th International Operational Modal Analysis Conference 213 May 13-15 Guimarães - Portugal MODIFICATIONS IN THE CURVE-FITTED ENHANCED FREQUENCY DOMAIN DECOMPOSITION METHOD FOR OMA IN THE PRESENCE
More informationAiro Interantional Research Journal September, 2013 Volume II, ISSN:
Airo Interantional Research Journal September, 2013 Volume II, ISSN: 2320-3714 Name of author- Navin Kumar Research scholar Department of Electronics BR Ambedkar Bihar University Muzaffarpur ABSTRACT Direction
More informationSound Waves and Beats
Sound Waves and Beats Computer 32 Sound waves consist of a series of air pressure variations. A Microphone diaphragm records these variations by moving in response to the pressure changes. The diaphragm
More informationChapter 4. Communication System Design and Parameters
Chapter 4 Communication System Design and Parameters CHAPTER 4 COMMUNICATION SYSTEM DESIGN AND PARAMETERS 4.1. Introduction In this chapter the design parameters and analysis factors are described which
More informationThe analysis of multi-channel sound reproduction algorithms using HRTF data
The analysis of multichannel sound reproduction algorithms using HRTF data B. Wiggins, I. PatersonStephens, P. Schillebeeckx Processing Applications Research Group University of Derby Derby, United Kingdom
More informationSound waves. septembre 2014 Audio signals and systems 1
Sound waves Sound is created by elastic vibrations or oscillations of particles in a particular medium. The vibrations are transmitted from particles to (neighbouring) particles: sound wave. Sound waves
More informationAudio Enhancement Using Remez Exchange Algorithm with DWT
Audio Enhancement Using Remez Exchange Algorithm with DWT Abstract: Audio enhancement became important when noise in signals causes loss of actual information. Many filters have been developed and still
More informationCopyright 2009 Pearson Education, Inc.
Chapter 16 Sound 16-1 Characteristics of Sound Sound can travel through h any kind of matter, but not through a vacuum. The speed of sound is different in different materials; in general, it is slowest
More informationThree Microphones Embedded System for Single Unknown Sound Source Localization
F.I. Bob / Carpathian Journal of Electronic and Computer Engineering 5 (2012) 19-24 19 Three Microphones Embedded System for Single Unknown Sound Source Localization Flaviu Ilie Bob Technical University
More informationTHE problem of acoustic echo cancellation (AEC) was
IEEE TRANSACTIONS ON SPEECH AND AUDIO PROCESSING, VOL. 13, NO. 6, NOVEMBER 2005 1231 Acoustic Echo Cancellation and Doubletalk Detection Using Estimated Loudspeaker Impulse Responses Per Åhgren Abstract
More informationSubband Analysis of Time Delay Estimation in STFT Domain
PAGE 211 Subband Analysis of Time Delay Estimation in STFT Domain S. Wang, D. Sen and W. Lu School of Electrical Engineering & Telecommunications University of ew South Wales, Sydney, Australia sh.wang@student.unsw.edu.au,
More information