Automatic Detection of Emergency Vehicles for Hearing Impaired Drivers

Save this PDF as:
 WORD  PNG  TXT  JPG

Size: px
Start display at page:

Download "Automatic Detection of Emergency Vehicles for Hearing Impaired Drivers"

Transcription

1 Automatic Detection of Emergency Vehicles for Hearing Impaired Drivers Sung-won ark and Jose Trevino Texas A&M University-Kingsville, EE/CS Department, MSC 92, Kingsville, TX TEL (36) , FAX (36) 593-2, Abstract In this paper a simple algorithm to detect an emergency vehicle s siren using the linear prediction is presented. By measuring the means and variances of the reflection coefficients in a preselected number of successive frames, automatic detection of emergency vehicle s siren is possible. It has been shown that only two coefficients are enough for successful detection. Due to the simplicity of the algorithm, it can be implemented easily on any Texas Instrument s TMS DSs. Keywords linear prediction, reflection coefficients, pattern recognition. * This research was supported by NASA Grant Number NAG I. Introduction Hearing impaired drivers or drivers who set the volume of their car s audio system very high cannot hear when an emergency vehicle, such as police car, fire engine, or ambulance, approaches. This may result in a collision or unnecessary delay. We propose an algorithm that can automatically detect an emergency vehicle s siren. It can be implemented easily on any TMS DSs. Once detection is made, it may be displayed visually on the dashboard, in practice. The siren usually has several dominant frequencies that typically last for several seconds. However, because of the Doppler effect, the frequencies are maintained within a tolerance for tens or hundreds of milliseconds. To find dominant frequencies, the linear prediction model is used. Linear predictive coding [] has been very successful for speech coding and efficient Durbin s recursive algorithm is used to find prediction coefficients. If the coefficients are maintained within a pre-selected tolerance for a pre-selected time, detection is made. In section II, the linear prediction is introduced. Efficient Durbin s method to compute linear prediction coefficients is explained in section III. The algorithm to detect a siren and some simulation results are explained in section IV. Finally, the conclusions are made in section V. II. Linear rediction In a variety of applications, it is desirable to compress a speech signal for efficient transmission or storage. For medium or low bit-rate speech coders, linear predictive coding (LC) [] is most widely used. Redundancy in a speech signal is removed by passing the signal through a speech analysis filter. The output of the filter, termed the residual error signal, has less redundancy than original speech signal and can be quantized by smaller number of bits than the original speech. The residual error signal along with the filter coefficients are transmitted to the receiver. At the receiver, the speech is reconstructed by passing the residual error signal through the synthesis filter. To model a human speech production system, the linear prediction model is used. This model not only works well for the speech but also for any kind of signal. Assume that the present sample of the signal is predicted by the past samples of the signal such that xn %( ) bxn ( ) + bxn ( 2) + L + bxn ( ) bxn ( m) () 2 m

2 where xn %( ) is the prediction of x(n), x(n k) is the k-th step previous sample, and {b m } are called the linear prediction coefficients. The error between the actual sample and the predicted one can be expressed as ε ( n) x( n) x% ( n) x( n) b x( n m). (2) m The sum of the squared error to be minimized is expressed as ( ) ( ) ( ) 2 E ε n x n bm x n m n n 2. (3) We would like to minimize the sum of the squared error. By setting to zero the derivative of E with respect to b m, one obtains n xn ( k) xn ( ) bxn m ( m) for k, 2, 3, Λ,. (4) Equation (4) results in unknowns in equations such that b xn ( kxn ) ( ) + b xn ( kxn ) ( 2) + L + b xn ( kxn ) ( ) 2 n n n xn ( kxn ) ( ) for k, 2, 3, Λ,. (5) n Let us assume that the signal is divided into frames each with N samples. If the length of each frame is short enough, the signal in the frame may be stationary. If there are N samples in the sequence indexed from to N- such that {x(n)} {x(), x(), x(2), Λ, x(n-2), x(n-)}, Equation (5) can be expressed in terms of matrix equation. r() r() L r ( 2) r ( ) b r() r() r() r( 3) r( 2) b 2 r(2) L M M O M M M M r ( 2) r ( 3) L r() r() b r ( ) r ( ) rp ( 2) L r() r() b r ( ) (6) where N k rk ( ) xnxn ( ) ( + k). (7) n To solve the matrix equation (6), the Durbin s method described in the next section is used. III. Durbin s Recursive Method The sum of squared errors of the -th order prediction (or simply the -th order prediction error) in Equation (3) can be rewritten as

3 E x( n) ε( n) bmx( n m) ε( n) n n (8) where the subscript of E denotes the order of prediction. Equation (4) can be rewritten as xn ( m) ε ( n) for m, 2, 3, Λ,. (9) n Because of Equation (9), the second summation of Equation (8) is zero. Thus, the final expression of the prediction error becomes E x( n) x( n) bmx( n m) n () m. r() b r() b 2 r(2) Λ b - r( ) b r() r() b r( m) We now want to develop a recursive method to solve Equation (6). Let us start from the order and increase it until the desired order reaches. When (i.e., when no prediction is made), the error is expressed from Equation (). E r(). () When, the error is expressed as E r() b r() (2) where the second subscript of b indicates that the prediction order in this case is. The solution of Equation (6) is b r()/r() κ (3) where κ is termed the reflection coefficient. Note that magnitude of κ is less than ( κ <) as r() is less than r(). Now the prediction error for becomes E r() κ r() r()[ κ 2 ] E [ κ 2 ]. (4) One can see that the prediction error E is smaller than E. When 2, Equations () and (6) can be combined in a single matrix equation r() r() r(2) E2 r() r() r() b 2 r(2) r() r() b 22 (5) Assume that the solution can be found recursively as shown below. b b κ b 2 2 b 22 (6) where κ 2 is the second reflection coefficient. The subscript 2 of b 2 and b 22 indicates that these are the second order linear prediction coefficients. Using Equation (6), Equation (5) becomes

4 r() r() r(2) E q2 E2 r() r() r() b κ b κ 2 2 r(2) r() r() q 2 E (7) where q 2 r(2) b r(). (8) Because q 2 κ 2 E from Equation (7), the second reflection coefficient becomes κ 2 q 2 /E. (9) The new prediction error for 2 becomes E 2 E κ 2 q 2 E [ κ 2 2 ]. (2) The linear prediction coefficients can be obtained using Equation (6) such that b 2 b κ 2 b and b 22 κ 2. (2) Now the recursive solution method for any prediction order is described below. Initial values: E r() b κ r()/e E E ( κ 2 ). With p 2, the following recursion is performed (i) q p r(p) (ii) κ p q p E( p ) p b r( p m) m( p ) (iii) b pp κ p (iv) b mp b m(p-) κ p b (p-m)(p-) for m, Λ, p (v) E p E (p-) [ κ p 2 ]. (vi) If p <, then increase p to p+ and go to (i). If p, then stop. IV. Results A signal is sampled at,25 Hz and divided into frames of 2 samples each. In each frame the first samples are used to compute reflection coefficients. In addition to reflection coefficients, we extracted linear prediction coefficients, roots of the prediction polynomial and the LS (line spectrum pair) frequencies [2]. All of them showed similar patterns. We chose reflection coefficients because of the simplicity. Fig. shows the first four reflection coefficients for 5 successive frames of an ambulance siren. The blue line (bottom) is for the first reflection coefficient, κ, and the green line (top) is for the second reflection coefficient, κ 2. As seen in Fig. (a), the first two reflection coefficients can be easily identified. The last two reflection coefficients are not distinguishable. In the case of wind noise, all four coefficients are mixed up. As the first two reflection coefficients have a distinct feature, the prediction order is chosen to be two from now on.

5 (a) ambulance siren (b) typical wind noise Fig. lots of the first four reflection coefficients of 5 successive frames of two signals: (a) ambulance siren (b) typical wind noise. blue (bottom) first reflection coefficient; green (top) second, red third, cyan fourth. The first two reflection coefficients for four different signals are computed and displayed in Fig. 2. The police siren shows that two coefficients are widely separated and each coefficient has relatively small standard deviation. The fire engine siren has about the same property as the police siren except for the second part. The second part, in fact, was the sound of a horn rather than a siren. In the case of speech signal, vowel sounds show the same property as sirens. Consonants have a property like noise. In both cases, the second reflection coefficient shows relatively large standard deviation. Finally, in the case of noise, two coefficients are mixed up and cannot be distinguishable. Based on the observations we made so far, the algorithm to detect emergency vehicle s siren is proposed and summarized below.. A signal is sampled at,25 samples/sec and divided into frames of 2 samples. 2. First two reflection coefficients are calculated from the first samples of each frame. 3. For the next successive frames, means and standard deviations of two reflection coefficients are computed. 4. Detection is made if the difference between the mean of the first reflection coefficient and the mean of the second coefficient is greater than the pre-selected number (.2 in this case) and the sum of standard deviations of two reflection coefficients is smaller than the pre-selected threshold (.3). Even though each frame is 2 samples long, only the first samples are collected and used for computation of reflection coefficients. This frees up a microprocessor to compute two reflection coefficients in each frame. Little more than 3 multiplies and 3 adds are required to compute two reflection coefficients. In every tenth frame, the difference between means and the sum of standard deviation of two reflection coefficients obtained during the past nine frames and the current frame are computed. If these parameters satisfy the condition described above (item 4), a detection is made and the result may be displayed on a dashboard. Occasionally, some other signals may cause a false alarm.

6 However, this will be displayed only for about.2 seconds. A user may not pay attention if detection is displayed for a short period of time (a) police car (b) fire engine (c) human speech (d) noise Fig. 2 The first two reflection coefficients for 5 frames of four signals are plotted. (a) police car siren (b) fire engine (c) human speech (d) noise blue (bottom) first reflection coefficient; green (top) second reflection coefficient By applying the algorithm to aforementioned four signals, detections were made and the results are plotted in Fig. 3. olice siren was detected successfully except for the duration of frames. First part of the fire engine siren was detected successfully. It should be noted that the second part of the signal was from a horn rather than a siren. Human speech results in false alarm occasionally. However, it is so unlikely that

7 human speech will be loud enough to be picked up by a microphone that would be supposed to pick up loud sirens and mounted somewhere outside a car (a) police car 5 5 (b) fire engine (c) human speech 5 5 (d) noise Fig. 3 Detection ( ) and no detection ( ) of siren was made over the duration of frames (about.2 seconds long) and plotted for 5 frames. V. Conclusions In this paper a simple algorithm to detect an emergency vehicle s siren using the linear prediction model for hearing impaired drivers is presented. By measuring the means and variances of reflection coefficients in a pre-selected number of successive frames, automatic detection of emergency vehicle s siren is possible. It has been shown that only two prediction coefficients are enough for successful detection. Due to the simplicity of the algorithm, it can be implemented easily on any TMS DSs. The algorithm is not foolproof and may result in false alarms. However, they may happen for just.2 or.4 seconds occasionally and can be ignored. A display of detection for several seconds will definitely get an attention of a driver. References [] Atal, B.S. and S.L. Hanauer, Speech analysis and synthesis by linear prediction of speech wave, J. Acoust. Soc. Am., vol. 5, pp , 97. [2] ark, S. and A. Ratanavarinchai, Simple Quantization of LS frequencies, roceedings of the IASTED/ISMM International conference on Modeling and Simulation, ittsburgh, A, April, 996 pp

Solutions to Exam in Speech Signal Processing EN2300

Solutions to Exam in Speech Signal Processing EN2300 Solutions to Exam in Speech Signal Processing EN23 Date: Thursday, Dec 2, 8: 3: Place: Allowed: Grades: Language: Solutions: Q34, Q36 Beta Math Handbook (or corresponding), calculator with empty memory.

More information

School Class Monitoring System Based on Audio Signal Processing

School Class Monitoring System Based on Audio Signal Processing C. R. Rashmi 1,,C.P.Shantala 2 andt.r.yashavanth 3 1 Department of CSE, PG Student, CIT, Gubbi, Tumkur, Karnataka, India. 2 Department of CSE, Vice Principal & HOD, CIT, Gubbi, Tumkur, Karnataka, India.

More information

Speech Signal Processing: An Overview

Speech Signal Processing: An Overview Speech Signal Processing: An Overview S. R. M. Prasanna Department of Electronics and Electrical Engineering Indian Institute of Technology Guwahati December, 2012 Prasanna (EMST Lab, EEE, IITG) Speech

More information

From Concept to Production in Secure Voice Communications

From Concept to Production in Secure Voice Communications From Concept to Production in Secure Voice Communications Earl E. Swartzlander, Jr. Electrical and Computer Engineering Department University of Texas at Austin Austin, TX 78712 Abstract In the 1970s secure

More information

Section 5.0 : Horn Physics. By Martin J. King, 6/29/08 Copyright 2008 by Martin J. King. All Rights Reserved.

Section 5.0 : Horn Physics. By Martin J. King, 6/29/08 Copyright 2008 by Martin J. King. All Rights Reserved. Section 5. : Horn Physics Section 5. : Horn Physics By Martin J. King, 6/29/8 Copyright 28 by Martin J. King. All Rights Reserved. Before discussing the design of a horn loaded loudspeaker system, it is

More information

This document is downloaded from DR-NTU, Nanyang Technological University Library, Singapore.

This document is downloaded from DR-NTU, Nanyang Technological University Library, Singapore. This document is downloaded from DR-NTU, Nanyang Technological University Library, Singapore. Title Transcription of polyphonic signals using fast filter bank( Accepted version ) Author(s) Foo, Say Wei;

More information

JPEG compression of monochrome 2D-barcode images using DCT coefficient distributions

JPEG compression of monochrome 2D-barcode images using DCT coefficient distributions Edith Cowan University Research Online ECU Publications Pre. JPEG compression of monochrome D-barcode images using DCT coefficient distributions Keng Teong Tan Hong Kong Baptist University Douglas Chai

More information

This unit will lay the groundwork for later units where the students will extend this knowledge to quadratic and exponential functions.

This unit will lay the groundwork for later units where the students will extend this knowledge to quadratic and exponential functions. Algebra I Overview View unit yearlong overview here Many of the concepts presented in Algebra I are progressions of concepts that were introduced in grades 6 through 8. The content presented in this course

More information

Voice---is analog in character and moves in the form of waves. 3-important wave-characteristics:

Voice---is analog in character and moves in the form of waves. 3-important wave-characteristics: Voice Transmission --Basic Concepts-- Voice---is analog in character and moves in the form of waves. 3-important wave-characteristics: Amplitude Frequency Phase Voice Digitization in the POTS Traditional

More information

Ericsson T18s Voice Dialing Simulator

Ericsson T18s Voice Dialing Simulator Ericsson T18s Voice Dialing Simulator Mauricio Aracena Kovacevic, Anna Dehlbom, Jakob Ekeberg, Guillaume Gariazzo, Eric Lästh and Vanessa Troncoso Dept. of Signals Sensors and Systems Royal Institute of

More information

Linear Predictive Coding

Linear Predictive Coding Linear Predictive Coding Jeremy Bradbury December 5, 2000 0 Outline I. Proposal II. Introduction A. Speech Coding B. Voice Coders C. LPC Overview III. Historical Perspective of Linear Predictive Coding

More information

APPLICATION NOTES: Dimming InGaN LED

APPLICATION NOTES: Dimming InGaN LED APPLICATION NOTES: Dimming InGaN LED Introduction: Indium gallium nitride (InGaN, In x Ga 1-x N) is a semiconductor material made of a mixture of gallium nitride (GaN) and indium nitride (InN). Indium

More information

Audio Engineering Society. Convention Paper. Presented at the 129th Convention 2010 November 4 7 San Francisco, CA, USA

Audio Engineering Society. Convention Paper. Presented at the 129th Convention 2010 November 4 7 San Francisco, CA, USA Audio Engineering Society Convention Paper Presented at the 129th Convention 2010 November 4 7 San Francisco, CA, USA The papers at this Convention have been selected on the basis of a submitted abstract

More information

Introduction and Comparison of Common Videoconferencing Audio Protocols I. Digital Audio Principles

Introduction and Comparison of Common Videoconferencing Audio Protocols I. Digital Audio Principles Introduction and Comparison of Common Videoconferencing Audio Protocols I. Digital Audio Principles Sound is an energy wave with frequency and amplitude. Frequency maps the axis of time, and amplitude

More information

Understanding CIC Compensation Filters

Understanding CIC Compensation Filters Understanding CIC Compensation Filters April 2007, ver. 1.0 Application Note 455 Introduction f The cascaded integrator-comb (CIC) filter is a class of hardware-efficient linear phase finite impulse response

More information

FFT Algorithms. Chapter 6. Contents 6.1

FFT Algorithms. Chapter 6. Contents 6.1 Chapter 6 FFT Algorithms Contents Efficient computation of the DFT............................................ 6.2 Applications of FFT................................................... 6.6 Computing DFT

More information

2x + y = 3. Since the second equation is precisely the same as the first equation, it is enough to find x and y satisfying the system

2x + y = 3. Since the second equation is precisely the same as the first equation, it is enough to find x and y satisfying the system 1. Systems of linear equations We are interested in the solutions to systems of linear equations. A linear equation is of the form 3x 5y + 2z + w = 3. The key thing is that we don t multiply the variables

More information

A Sound Analysis and Synthesis System for Generating an Instrumental Piri Song

A Sound Analysis and Synthesis System for Generating an Instrumental Piri Song , pp.347-354 http://dx.doi.org/10.14257/ijmue.2014.9.8.32 A Sound Analysis and Synthesis System for Generating an Instrumental Piri Song Myeongsu Kang and Jong-Myon Kim School of Electrical Engineering,

More information

Khalid Sayood and Martin C. Rost Department of Electrical Engineering University of Nebraska

Khalid Sayood and Martin C. Rost Department of Electrical Engineering University of Nebraska PROBLEM STATEMENT A ROBUST COMPRESSION SYSTEM FOR LOW BIT RATE TELEMETRY - TEST RESULTS WITH LUNAR DATA Khalid Sayood and Martin C. Rost Department of Electrical Engineering University of Nebraska The

More information

Enhancing the SNR of the Fiber Optic Rotation Sensor using the LMS Algorithm

Enhancing the SNR of the Fiber Optic Rotation Sensor using the LMS Algorithm 1 Enhancing the SNR of the Fiber Optic Rotation Sensor using the LMS Algorithm Hani Mehrpouyan, Student Member, IEEE, Department of Electrical and Computer Engineering Queen s University, Kingston, Ontario,

More information

Trend and Seasonal Components

Trend and Seasonal Components Chapter 2 Trend and Seasonal Components If the plot of a TS reveals an increase of the seasonal and noise fluctuations with the level of the process then some transformation may be necessary before doing

More information

Log-Likelihood Ratio-based Relay Selection Algorithm in Wireless Network

Log-Likelihood Ratio-based Relay Selection Algorithm in Wireless Network Recent Advances in Electrical Engineering and Electronic Devices Log-Likelihood Ratio-based Relay Selection Algorithm in Wireless Network Ahmed El-Mahdy and Ahmed Walid Faculty of Information Engineering

More information

Time series Forecasting using Holt-Winters Exponential Smoothing

Time series Forecasting using Holt-Winters Exponential Smoothing Time series Forecasting using Holt-Winters Exponential Smoothing Prajakta S. Kalekar(04329008) Kanwal Rekhi School of Information Technology Under the guidance of Prof. Bernard December 6, 2004 Abstract

More information

physics 1/12/2016 Chapter 20 Lecture Chapter 20 Traveling Waves

physics 1/12/2016 Chapter 20 Lecture Chapter 20 Traveling Waves Chapter 20 Lecture physics FOR SCIENTISTS AND ENGINEERS a strategic approach THIRD EDITION randall d. knight Chapter 20 Traveling Waves Chapter Goal: To learn the basic properties of traveling waves. Slide

More information

Sound absorption and acoustic surface impedance

Sound absorption and acoustic surface impedance Sound absorption and acoustic surface impedance CHRISTER HEED SD2165 Stockholm October 2008 Marcus Wallenberg Laboratoriet för Ljud- och Vibrationsforskning Sound absorption and acoustic surface impedance

More information

ACOUSTICAL CONSIDERATIONS FOR EFFECTIVE EMERGENCY ALARM SYSTEMS IN AN INDUSTRIAL SETTING

ACOUSTICAL CONSIDERATIONS FOR EFFECTIVE EMERGENCY ALARM SYSTEMS IN AN INDUSTRIAL SETTING ACOUSTICAL CONSIDERATIONS FOR EFFECTIVE EMERGENCY ALARM SYSTEMS IN AN INDUSTRIAL SETTING Dennis P. Driscoll, P.E. and David C. Byrne, CCC-A Associates in Acoustics, Inc. Evergreen, Colorado Telephone (303)

More information

VoIP Technologies Lecturer : Dr. Ala Khalifeh Lecture 4 : Voice codecs (Cont.)

VoIP Technologies Lecturer : Dr. Ala Khalifeh Lecture 4 : Voice codecs (Cont.) VoIP Technologies Lecturer : Dr. Ala Khalifeh Lecture 4 : Voice codecs (Cont.) 1 Remember first the big picture VoIP network architecture and some terminologies Voice coders 2 Audio and voice quality measuring

More information

By choosing to view this document, you agree to all provisions of the copyright laws protecting it.

By choosing to view this document, you agree to all provisions of the copyright laws protecting it. This material is posted here with permission of the IEEE Such permission of the IEEE does not in any way imply IEEE endorsement of any of Helsinki University of Technology's products or services Internal

More information

The Sonometer The Resonant String and Timbre Change after plucking

The Sonometer The Resonant String and Timbre Change after plucking The Sonometer The Resonant String and Timbre Change after plucking EQUIPMENT Pasco sonometers (pick up 5 from teaching lab) and 5 kits to go with them BK Precision function generators and Tenma oscilloscopes

More information

Image Compression through DCT and Huffman Coding Technique

Image Compression through DCT and Huffman Coding Technique International Journal of Current Engineering and Technology E-ISSN 2277 4106, P-ISSN 2347 5161 2015 INPRESSCO, All Rights Reserved Available at http://inpressco.com/category/ijcet Research Article Rahul

More information

Tutorial about the VQR (Voice Quality Restoration) technology

Tutorial about the VQR (Voice Quality Restoration) technology Tutorial about the VQR (Voice Quality Restoration) technology Ing Oscar Bonello, Solidyne Fellow Audio Engineering Society, USA INTRODUCTION Telephone communications are the most widespread form of transport

More information

The Effect of Network Cabling on Bit Error Rate Performance. By Paul Kish NORDX/CDT

The Effect of Network Cabling on Bit Error Rate Performance. By Paul Kish NORDX/CDT The Effect of Network Cabling on Bit Error Rate Performance By Paul Kish NORDX/CDT Table of Contents Introduction... 2 Probability of Causing Errors... 3 Noise Sources Contributing to Errors... 4 Bit Error

More information

Understanding Alarm Systems

Understanding Alarm Systems Understanding Alarm Systems A false alarm occurs when an alarm signal designed to elicit an immediate emergency Law Enforcement, Fire, or Medical response is activated, when in fact no emergency exists.

More information

Principal components analysis

Principal components analysis CS229 Lecture notes Andrew Ng Part XI Principal components analysis In our discussion of factor analysis, we gave a way to model data x R n as approximately lying in some k-dimension subspace, where k

More information

Vehicle Tracking System Robust to Changes in Environmental Conditions

Vehicle Tracking System Robust to Changes in Environmental Conditions INORMATION & COMMUNICATIONS Vehicle Tracking System Robust to Changes in Environmental Conditions Yasuo OGIUCHI*, Masakatsu HIGASHIKUBO, Kenji NISHIDA and Takio KURITA Driving Safety Support Systems (DSSS)

More information

Advanced Speech-Audio Processing in Mobile Phones and Hearing Aids

Advanced Speech-Audio Processing in Mobile Phones and Hearing Aids Advanced Speech-Audio Processing in Mobile Phones and Hearing Aids Synergies and Distinctions Peter Vary RWTH Aachen University Institute of Communication Systems WASPAA, October 23, 2013 Mohonk Mountain

More information

PCM Encoding and Decoding:

PCM Encoding and Decoding: PCM Encoding and Decoding: Aim: Introduction to PCM encoding and decoding. Introduction: PCM Encoding: The input to the PCM ENCODER module is an analog message. This must be constrained to a defined bandwidth

More information

2-1 Position, Displacement, and Distance

2-1 Position, Displacement, and Distance 2-1 Position, Displacement, and Distance In describing an object s motion, we should first talk about position where is the object? A position is a vector because it has both a magnitude and a direction:

More information

Sound Pressure Measurement

Sound Pressure Measurement Objectives: Sound Pressure Measurement 1. Become familiar with hardware and techniques to measure sound pressure 2. Measure the sound level of various sizes of fan modules 3. Calculate the signal-to-noise

More information

STUDY OF MUTUAL INFORMATION IN PERCEPTUAL CODING WITH APPLICATION FOR LOW BIT-RATE COMPRESSION

STUDY OF MUTUAL INFORMATION IN PERCEPTUAL CODING WITH APPLICATION FOR LOW BIT-RATE COMPRESSION STUDY OF MUTUAL INFORMATION IN PERCEPTUAL CODING WITH APPLICATION FOR LOW BIT-RATE COMPRESSION Adiel Ben-Shalom, Michael Werman School of Computer Science Hebrew University Jerusalem, Israel. {chopin,werman}@cs.huji.ac.il

More information

Ultrasound Distance Measurement

Ultrasound Distance Measurement Final Project Report E3390 Electronic Circuits Design Lab Ultrasound Distance Measurement Yiting Feng Izel Niyage Asif Quyyum Submitted in partial fulfillment of the requirements for the Bachelor of Science

More information

SWISS ARMY KNIFE INDICATOR John F. Ehlers

SWISS ARMY KNIFE INDICATOR John F. Ehlers SWISS ARMY KNIFE INDICATOR John F. Ehlers The indicator I describe in this article does all the common functions of the usual indicators, such as smoothing and momentum generation. It also does some unusual

More information

Method To Solve Linear, Polynomial, or Absolute Value Inequalities:

Method To Solve Linear, Polynomial, or Absolute Value Inequalities: Solving Inequalities An inequality is the result of replacing the = sign in an equation with ,, or. For example, 3x 2 < 7 is a linear inequality. We call it linear because if the < were replaced with

More information

POTENTIAL OF STATE-FEEDBACK CONTROL FOR MACHINE TOOLS DRIVES

POTENTIAL OF STATE-FEEDBACK CONTROL FOR MACHINE TOOLS DRIVES POTENTIAL OF STATE-FEEDBACK CONTROL FOR MACHINE TOOLS DRIVES L. Novotny 1, P. Strakos 1, J. Vesely 1, A. Dietmair 2 1 Research Center of Manufacturing Technology, CTU in Prague, Czech Republic 2 SW, Universität

More information

Stream Boost: All About That Bass

Stream Boost: All About That Bass Carreen Pederson, M.A., & Alyson Gruhlke, Au.D. Stream Boost is an automatic feature that activates hearing aid settings optimized for high-quality streamed audio. Stream Boost settings are not part of

More information

Transmission Line and Back Loaded Horn Physics

Transmission Line and Back Loaded Horn Physics Introduction By Martin J. King, 3/29/3 Copyright 23 by Martin J. King. All Rights Reserved. In order to differentiate between a transmission line and a back loaded horn, it is really important to understand

More information

a. CSMA/CD is a random-access protocol. b. Polling is a controlled-access protocol. c. TDMA is a channelization protocol.

a. CSMA/CD is a random-access protocol. b. Polling is a controlled-access protocol. c. TDMA is a channelization protocol. CHAPTER 12 PRACTICE SET Questions Q12-1. The answer is CSM/CD. a. CSMA/CD is a random-access protocol. b. Polling is a controlled-access protocol. c. TDMA is a channelization protocol. Q12-3. The answer

More information

Broadband Networks. Prof. Dr. Abhay Karandikar. Electrical Engineering Department. Indian Institute of Technology, Bombay. Lecture - 29.

Broadband Networks. Prof. Dr. Abhay Karandikar. Electrical Engineering Department. Indian Institute of Technology, Bombay. Lecture - 29. Broadband Networks Prof. Dr. Abhay Karandikar Electrical Engineering Department Indian Institute of Technology, Bombay Lecture - 29 Voice over IP So, today we will discuss about voice over IP and internet

More information

Computer Networks and Internets, 5e Chapter 6 Information Sources and Signals. Introduction

Computer Networks and Internets, 5e Chapter 6 Information Sources and Signals. Introduction Computer Networks and Internets, 5e Chapter 6 Information Sources and Signals Modified from the lecture slides of Lami Kaya (LKaya@ieee.org) for use CECS 474, Fall 2008. 2009 Pearson Education Inc., Upper

More information

TTT4110 Information and Signal Theory Solution to exam

TTT4110 Information and Signal Theory Solution to exam Norwegian University of Science and Technology Department of Electronics and Telecommunications TTT4 Information and Signal Theory Solution to exam Problem I (a The frequency response is found by taking

More information

Sampling Theorem Notes. Recall: That a time sampled signal is like taking a snap shot or picture of signal periodically.

Sampling Theorem Notes. Recall: That a time sampled signal is like taking a snap shot or picture of signal periodically. Sampling Theorem We will show that a band limited signal can be reconstructed exactly from its discrete time samples. Recall: That a time sampled signal is like taking a snap shot or picture of signal

More information

Lecture 5 Least-squares

Lecture 5 Least-squares EE263 Autumn 2007-08 Stephen Boyd Lecture 5 Least-squares least-squares (approximate) solution of overdetermined equations projection and orthogonality principle least-squares estimation BLUE property

More information

SOFTWARE FOR GENERATION OF SPECTRUM COMPATIBLE TIME HISTORY

SOFTWARE FOR GENERATION OF SPECTRUM COMPATIBLE TIME HISTORY 3 th World Conference on Earthquake Engineering Vancouver, B.C., Canada August -6, 24 Paper No. 296 SOFTWARE FOR GENERATION OF SPECTRUM COMPATIBLE TIME HISTORY ASHOK KUMAR SUMMARY One of the important

More information

Figure1. Acoustic feedback in packet based video conferencing system

Figure1. Acoustic feedback in packet based video conferencing system Real-Time Howling Detection for Hands-Free Video Conferencing System Mi Suk Lee and Do Young Kim Future Internet Research Department ETRI, Daejeon, Korea {lms, dyk}@etri.re.kr Abstract: This paper presents

More information

PHASE ESTIMATION ALGORITHM FOR FREQUENCY HOPPED BINARY PSK AND DPSK WAVEFORMS WITH SMALL NUMBER OF REFERENCE SYMBOLS

PHASE ESTIMATION ALGORITHM FOR FREQUENCY HOPPED BINARY PSK AND DPSK WAVEFORMS WITH SMALL NUMBER OF REFERENCE SYMBOLS PHASE ESTIMATION ALGORITHM FOR FREQUENCY HOPPED BINARY PSK AND DPSK WAVEFORMS WITH SMALL NUM OF REFERENCE SYMBOLS Benjamin R. Wiederholt The MITRE Corporation Bedford, MA and Mario A. Blanco The MITRE

More information

In-Flight File Transfer. Project Plan: Red Group

In-Flight File Transfer. Project Plan: Red Group Project Course in Signal Processing and Digital Communications (EQ2430) Project in Wireless Communication (EQ2440) In-Flight File Transfer Project Plan: Red Group Project supervisor: Per Zetterberg Project

More information

Signal to Noise Instrumental Excel Assignment

Signal to Noise Instrumental Excel Assignment Signal to Noise Instrumental Excel Assignment Instrumental methods, as all techniques involved in physical measurements, are limited by both the precision and accuracy. The precision and accuracy of a

More information

8. Linear least-squares

8. Linear least-squares 8. Linear least-squares EE13 (Fall 211-12) definition examples and applications solution of a least-squares problem, normal equations 8-1 Definition overdetermined linear equations if b range(a), cannot

More information

Univariate Regression

Univariate Regression Univariate Regression Correlation and Regression The regression line summarizes the linear relationship between 2 variables Correlation coefficient, r, measures strength of relationship: the closer r is

More information

Implementing an In-Service, Non- Intrusive Measurement Device in Telecommunication Networks Using the TMS320C31

Implementing an In-Service, Non- Intrusive Measurement Device in Telecommunication Networks Using the TMS320C31 Disclaimer: This document was part of the First European DSP Education and Research Conference. It may have been written by someone whose native language is not English. TI assumes no liability for the

More information

Algorithms for Interference Sensing in Optical CDMA Networks

Algorithms for Interference Sensing in Optical CDMA Networks Algorithms for Interference Sensing in Optical CDMA Networks Purushotham Kamath, Joseph D. Touch and Joseph A. Bannister {pkamath, touch, joseph}@isi.edu Information Sciences Institute, University of Southern

More information

Comparison different seat-spine transfer functions for vibrational comfort monitoring of car passengers

Comparison different seat-spine transfer functions for vibrational comfort monitoring of car passengers Comparison different seat-spine transfer functions for vibrational comfort monitoring of car passengers Massimo Cavacece 1, Daniele Carnevale 3, Ettore Pennestrì 2, Pier Paolo Valentini 2, Fabrizio Scirè,

More information

Effects of Pronunciation Practice System Based on Personalized CG Animations of Mouth Movement Model

Effects of Pronunciation Practice System Based on Personalized CG Animations of Mouth Movement Model Effects of Pronunciation Practice System Based on Personalized CG Animations of Mouth Movement Model Kohei Arai 1 Graduate School of Science and Engineering Saga University Saga City, Japan Mariko Oda

More information

12.5: CHI-SQUARE GOODNESS OF FIT TESTS

12.5: CHI-SQUARE GOODNESS OF FIT TESTS 125: Chi-Square Goodness of Fit Tests CD12-1 125: CHI-SQUARE GOODNESS OF FIT TESTS In this section, the χ 2 distribution is used for testing the goodness of fit of a set of data to a specific probability

More information

ADAPTIVE ALGORITHMS FOR ACOUSTIC ECHO CANCELLATION IN SPEECH PROCESSING

ADAPTIVE ALGORITHMS FOR ACOUSTIC ECHO CANCELLATION IN SPEECH PROCESSING www.arpapress.com/volumes/vol7issue1/ijrras_7_1_05.pdf ADAPTIVE ALGORITHMS FOR ACOUSTIC ECHO CANCELLATION IN SPEECH PROCESSING 1,* Radhika Chinaboina, 1 D.S.Ramkiran, 2 Habibulla Khan, 1 M.Usha, 1 B.T.P.Madhav,

More information

SPP ACE Diversity Interchange (ADI) System Requirements V1.6

SPP ACE Diversity Interchange (ADI) System Requirements V1.6 SPP ACE Diversity Interchange (ADI) System Requirements V1.6 Introduction The SPP ADI program will be an application running within the SPP EMS. Input data will be coming to SPP from participating members

More information

Thirukkural - A Text-to-Speech Synthesis System

Thirukkural - A Text-to-Speech Synthesis System Thirukkural - A Text-to-Speech Synthesis System G. L. Jayavardhana Rama, A. G. Ramakrishnan, M Vijay Venkatesh, R. Murali Shankar Department of Electrical Engg, Indian Institute of Science, Bangalore 560012,

More information

Email: tjohn@mail.nplindia.ernet.in

Email: tjohn@mail.nplindia.ernet.in USE OF VIRTUAL INSTRUMENTS IN RADIO AND ATMOSPHERIC EXPERIMENTS P.N. VIJAYAKUMAR, THOMAS JOHN AND S.C. GARG RADIO AND ATMOSPHERIC SCIENCE DIVISION, NATIONAL PHYSICAL LABORATORY, NEW DELHI 110012, INDIA

More information

Analysis of Filter Coefficient Precision on LMS Algorithm Performance for G.165/G.168 Echo Cancellation

Analysis of Filter Coefficient Precision on LMS Algorithm Performance for G.165/G.168 Echo Cancellation Application Report SPRA561 - February 2 Analysis of Filter Coefficient Precision on LMS Algorithm Performance for G.165/G.168 Echo Cancellation Zhaohong Zhang Gunter Schmer C6 Applications ABSTRACT This

More information

SPEECH INTELLIGIBILITY and Fire Alarm Voice Communication Systems

SPEECH INTELLIGIBILITY and Fire Alarm Voice Communication Systems SPEECH INTELLIGIBILITY and Fire Alarm Voice Communication Systems WILLIAM KUFFNER, M.A. Sc., P.Eng, PMP Senior Fire Protection Engineer Director Fire Protection Engineering October 30, 2013 Code Reference

More information

Vision based Vehicle Tracking using a high angle camera

Vision based Vehicle Tracking using a high angle camera Vision based Vehicle Tracking using a high angle camera Raúl Ignacio Ramos García Dule Shu gramos@clemson.edu dshu@clemson.edu Abstract A vehicle tracking and grouping algorithm is presented in this work

More information

Time Domain and Frequency Domain Techniques For Multi Shaker Time Waveform Replication

Time Domain and Frequency Domain Techniques For Multi Shaker Time Waveform Replication Time Domain and Frequency Domain Techniques For Multi Shaker Time Waveform Replication Thomas Reilly Data Physics Corporation 1741 Technology Drive, Suite 260 San Jose, CA 95110 (408) 216-8440 This paper

More information

Analog and Digital Filters Anthony Garvert November 13, 2015

Analog and Digital Filters Anthony Garvert November 13, 2015 Analog and Digital Filters Anthony Garvert November 13, 2015 Abstract In circuit analysis and performance, a signal transmits some form of information, such as a voltage or current. However, over a range

More information

Anomaly Detection in Predictive Maintenance

Anomaly Detection in Predictive Maintenance Anomaly Detection in Predictive Maintenance Anomaly Detection with Time Series Analysis Phil Winters Iris Adae Rosaria Silipo Phil.Winters@knime.com Iris.Adae@uni-konstanz.de Rosaria.Silipo@knime.com Copyright

More information

Digital Radar for Collision Avoidance and Automatic Cruise Control in Transportation

Digital Radar for Collision Avoidance and Automatic Cruise Control in Transportation Digital Radar for Collision Avoidance and Automatic Cruise Control in Transportation Rabindranath Bera, Sourav Dhar, Debdatta Kandar Sikkim Manipal Institte of Technology, Sikkim Manipal University, majitar,

More information

Epipolar Geometry. Readings: See Sections 10.1 and 15.6 of Forsyth and Ponce. Right Image. Left Image. e(p ) Epipolar Lines. e(q ) q R.

Epipolar Geometry. Readings: See Sections 10.1 and 15.6 of Forsyth and Ponce. Right Image. Left Image. e(p ) Epipolar Lines. e(q ) q R. Epipolar Geometry We consider two perspective images of a scene as taken from a stereo pair of cameras (or equivalently, assume the scene is rigid and imaged with a single camera from two different locations).

More information

Means, standard deviations and. and standard errors

Means, standard deviations and. and standard errors CHAPTER 4 Means, standard deviations and standard errors 4.1 Introduction Change of units 4.2 Mean, median and mode Coefficient of variation 4.3 Measures of variation 4.4 Calculating the mean and standard

More information

ECE302 Spring 2006 HW4 Solutions February 6, 2006 1

ECE302 Spring 2006 HW4 Solutions February 6, 2006 1 ECE302 Spring 2006 HW4 Solutions February 6, 2006 1 Solutions to HW4 Note: Most of these solutions were generated by R. D. Yates and D. J. Goodman, the authors of our textbook. I have added comments in

More information

Non-Data Aided Carrier Offset Compensation for SDR Implementation

Non-Data Aided Carrier Offset Compensation for SDR Implementation Non-Data Aided Carrier Offset Compensation for SDR Implementation Anders Riis Jensen 1, Niels Terp Kjeldgaard Jørgensen 1 Kim Laugesen 1, Yannick Le Moullec 1,2 1 Department of Electronic Systems, 2 Center

More information

MUSIC-like Processing of Pulsed Continuous Wave Signals in Active Sonar Experiments

MUSIC-like Processing of Pulsed Continuous Wave Signals in Active Sonar Experiments 23rd European Signal Processing Conference EUSIPCO) MUSIC-like Processing of Pulsed Continuous Wave Signals in Active Sonar Experiments Hock Siong LIM hales Research and echnology, Singapore hales Solutions

More information

LOW COST HARDWARE IMPLEMENTATION FOR DIGITAL HEARING AID USING

LOW COST HARDWARE IMPLEMENTATION FOR DIGITAL HEARING AID USING LOW COST HARDWARE IMPLEMENTATION FOR DIGITAL HEARING AID USING RasPi Kaveri Ratanpara 1, Priyan Shah 2 1 Student, M.E Biomedical Engineering, Government Engineering college, Sector-28, Gandhinagar (Gujarat)-382028,

More information

application note Directional Microphone Applications Introduction Directional Hearing Aids

application note Directional Microphone Applications Introduction Directional Hearing Aids APPLICATION NOTE AN-4 Directional Microphone Applications Introduction The inability to understand speech in noisy environments is a significant problem for hearing impaired individuals. An omnidirectional

More information

Developing an Isolated Word Recognition System in MATLAB

Developing an Isolated Word Recognition System in MATLAB MATLAB Digest Developing an Isolated Word Recognition System in MATLAB By Daryl Ning Speech-recognition technology is embedded in voice-activated routing systems at customer call centres, voice dialling

More information

Partial Fractions. Combining fractions over a common denominator is a familiar operation from algebra:

Partial Fractions. Combining fractions over a common denominator is a familiar operation from algebra: Partial Fractions Combining fractions over a common denominator is a familiar operation from algebra: From the standpoint of integration, the left side of Equation 1 would be much easier to work with than

More information

AP1 Waves. (A) frequency (B) wavelength (C) speed (D) intensity. Answer: (A) and (D) frequency and intensity.

AP1 Waves. (A) frequency (B) wavelength (C) speed (D) intensity. Answer: (A) and (D) frequency and intensity. 1. A fire truck is moving at a fairly high speed, with its siren emitting sound at a specific pitch. As the fire truck recedes from you which of the following characteristics of the sound wave from the

More information

Introduction Ericsson Handheld Telephone 1341-B

Introduction Ericsson Handheld Telephone 1341-B Ericsson Handheld Telephone 1341-B 2 Contents General 5 The Mobile Telephone Network 6 Base Station and Cell 7 Radio Channels 7 Radio Coverage 8 Transmission Control and Communication 9 Quality Control

More information

ADVANCED APPLICATIONS OF ELECTRICAL ENGINEERING

ADVANCED APPLICATIONS OF ELECTRICAL ENGINEERING Development of a Software Tool for Performance Evaluation of MIMO OFDM Alamouti using a didactical Approach as a Educational and Research support in Wireless Communications JOSE CORDOVA, REBECA ESTRADA

More information

Convention Paper Presented at the 118th Convention 2005 May 28 31 Barcelona, Spain

Convention Paper Presented at the 118th Convention 2005 May 28 31 Barcelona, Spain Audio Engineering Society Convention Paper Presented at the 118th Convention 25 May 28 31 Barcelona, Spain 6431 This convention paper has been reproduced from the author s advance manuscript, without editing,

More information

AUDIO CODING: BASICS AND STATE OF THE ART

AUDIO CODING: BASICS AND STATE OF THE ART AUDIO CODING: BASICS AND STATE OF THE ART PACS REFERENCE: 43.75.CD Brandenburg, Karlheinz Fraunhofer Institut Integrierte Schaltungen, Arbeitsgruppe Elektronische Medientechnolgie Am Helmholtzring 1 98603

More information

Tracking Moving Objects In Video Sequences Yiwei Wang, Robert E. Van Dyck, and John F. Doherty Department of Electrical Engineering The Pennsylvania State University University Park, PA16802 Abstract{Object

More information

Probability and Random Variables. Generation of random variables (r.v.)

Probability and Random Variables. Generation of random variables (r.v.) Probability and Random Variables Method for generating random variables with a specified probability distribution function. Gaussian And Markov Processes Characterization of Stationary Random Process Linearly

More information

Auto-Tuning Using Fourier Coefficients

Auto-Tuning Using Fourier Coefficients Auto-Tuning Using Fourier Coefficients Math 56 Tom Whalen May 20, 2013 The Fourier transform is an integral part of signal processing of any kind. To be able to analyze an input signal as a superposition

More information

System Identification for Acoustic Comms.:

System Identification for Acoustic Comms.: System Identification for Acoustic Comms.: New Insights and Approaches for Tracking Sparse and Rapidly Fluctuating Channels Weichang Li and James Preisig Woods Hole Oceanographic Institution The demodulation

More information

Doppler Effect Plug-in in Music Production and Engineering

Doppler Effect Plug-in in Music Production and Engineering , pp.287-292 http://dx.doi.org/10.14257/ijmue.2014.9.8.26 Doppler Effect Plug-in in Music Production and Engineering Yoemun Yun Department of Applied Music, Chungwoon University San 29, Namjang-ri, Hongseong,

More information

Hardware Implementation of Probabilistic State Machine for Word Recognition

Hardware Implementation of Probabilistic State Machine for Word Recognition IJECT Vo l. 4, Is s u e Sp l - 5, Ju l y - Se p t 2013 ISSN : 2230-7109 (Online) ISSN : 2230-9543 (Print) Hardware Implementation of Probabilistic State Machine for Word Recognition 1 Soorya Asokan, 2

More information

Multichannel stereophonic sound system with and without accompanying picture

Multichannel stereophonic sound system with and without accompanying picture Recommendation ITU-R BS.775-2 (07/2006) Multichannel stereophonic sound system with and without accompanying picture BS Series Broadcasting service (sound) ii Rec. ITU-R BS.775-2 Foreword The role of the

More information

General Framework for an Iterative Solution of Ax b. Jacobi s Method

General Framework for an Iterative Solution of Ax b. Jacobi s Method 2.6 Iterative Solutions of Linear Systems 143 2.6 Iterative Solutions of Linear Systems Consistent linear systems in real life are solved in one of two ways: by direct calculation (using a matrix factorization,

More information

3.1 State Space Models

3.1 State Space Models 31 State Space Models In this section we study state space models of continuous-time linear systems The corresponding results for discrete-time systems, obtained via duality with the continuous-time models,

More information

VISUAL ALGEBRA FOR COLLEGE STUDENTS. Laurie J. Burton Western Oregon University

VISUAL ALGEBRA FOR COLLEGE STUDENTS. Laurie J. Burton Western Oregon University VISUAL ALGEBRA FOR COLLEGE STUDENTS Laurie J. Burton Western Oregon University VISUAL ALGEBRA FOR COLLEGE STUDENTS TABLE OF CONTENTS Welcome and Introduction 1 Chapter 1: INTEGERS AND INTEGER OPERATIONS

More information

UNIVERSITY OF CALICUT

UNIVERSITY OF CALICUT UNIVERSITY OF CALICUT SCHOOL OF DISTANCE EDUCATION BMMC (2011 Admission) V SEMESTER CORE COURSE AUDIO RECORDING & EDITING QUESTION BANK 1. Sound measurement a) Decibel b) frequency c) Wave 2. Acoustics

More information