# L9: Cepstral analysis

Save this PDF as:

Size: px
Start display at page:

## Transcription

1 L9: Cepstral analysis The cepstrum Homomorphic filtering The cepstrum and voicing/pitch detection Linear prediction cepstral coefficients Mel frequency cepstral coefficients This lecture is based on [Taylor, 2009, ch. 12; Rabiner and Schafer, 2007, ch. 5; Rabiner and Schafer, 1978, ch. 7 ] Introduction to Speech Processing Ricardo Gutierrez-Osuna 1

2 Definition The cepstrum The cepstrum is defined as the inverse DFT of the log magnitude of the DFT of a signal c n = F 1 log F x n where F is the DFT and F 1 is the IDFT For a windowed frame of speech y n, the cepstrum is N 1 N 1 c n = log x n e j2π N kn n=0 n=0 e j2π N kn x n X k X k DFT log IDFT c n Introduction to Speech Processing Ricardo Gutierrez-Osuna 2

3 F x n log F x n F 1 log F x n [Taylor, 2009] Introduction to Speech Processing Ricardo Gutierrez-Osuna 3

4 Motivation Consider the magnitude spectrum F x n of the periodic signal in the previous slide This spectra contains harmonics at evenly spaced intervals, whose magnitude decreases quite quickly as frequency increases By calculating the log spectrum, however, we can compress the dynamic range and reduce amplitude differences in the harmonics Now imagine we were told that the log spectra was a waveform In this case, we would we would describe it as quasi-periodic with some form of amplitude modulation To separate both components, we could then employ the DFT We would then expect the DFT to show A large spike around the period of the signal, and Some low-frequency components due to the amplitude modulation A simple filter would then allow us to separate both components (see next slide) Introduction to Speech Processing Ricardo Gutierrez-Osuna 4

5 Liftering in the cepstral domain [Rabiner & Schafer, 2007] Introduction to Speech Processing Ricardo Gutierrez-Osuna 5

6 Cepstral analysis as deconvolution As you recall, the speech signal can be modeled as the convolution of glottal source u n, vocal tract v n, and radiation r n y n = u n v n r n Because these signals are convolved, they cannot be easily separated in the time domain We can however perform the separation as follows For convenience, we combine v n = v n r n, which leads to y n = u n v n Taking the Fourier transform Y e jω = U e jω V e jω If we now take the log of the magnitude, we obtain log Y e jω = log U e jω + log V e jω which shows that source and filter are now just added together We can now return to the time domain through the inverse FT c n = c u n + c v n Introduction to Speech Processing Ricardo Gutierrez-Osuna 6

7 Where does the term cepstrum come from? The crucial observation leading to the cepstrum terminology is that the log spectrum can be treated as a waveform and subjected to further Fourier analysis The independent variable of the cepstrum is nominally time since it is the IDFT of a log-spectrum, but is interpreted as a frequency since we are treating the log spectrum as a waveform To emphasize this interchanging of domains, Bogert, Healy and Tukey (1960) coined the term cepstrum by swapping the order of the letters in the word spectrum Likewise, the name of the independent variable of the cepstrum is known as a quefrency, and the linear filtering operation in the previous slide is known as liftering Introduction to Speech Processing Ricardo Gutierrez-Osuna 7

8 Discussion If we take the DFT of a signal and then take the inverse DFT of that, we of course get back the original signal (assuming it is stationary) The cepstrum calculation is different in two ways First, we only use magnitude information, and throw away the phase Second, we take the IDFT of the log-magnitude which is already very different since the log operation emphasizes the periodicity of the harmonics The cepstrum is useful because it separates source and filter If we are interested in the glottal excitation, we keep the high coefficients If we are interested in the vocal tract, we keep the low coefficients Truncating the cepstrum at different quefrency values allows us to preserve different amounts of spectral detail (see next slide) Introduction to Speech Processing Ricardo Gutierrez-Osuna 8

9 K=50 K=30 K=20 K=10 Frequency (Hz) [Taylor, 2009] Introduction to Speech Processing Ricardo Gutierrez-Osuna 9

10 Note from the previous slide that cepstral coefficients are a very compact representation of the spectral envelope It also turns out that cepstral coefficients are (to a large extent) uncorrelated This is very useful for statistical modeling because we only need to store their mean and variances but not their covariances For these reasons, cepstral coefficients are widely used in speech recognition, generally combined with a perceptual auditory scale, as we see next Introduction to Speech Processing Ricardo Gutierrez-Osuna 10

11 ex9p1.m Computing the cepstrum Liftering vs. linear prediction Show uncorrelatedness of cepstrum Introduction to Speech Processing Ricardo Gutierrez-Osuna 11

12 Homomorphic filtering Cepstral analysis is a special case of homomorphic filtering Homomorphic filtering is a generalized technique involving (1) a nonlinear mapping to a different domain where (2) linear filters are applied, followed by (3) mapping back to the original domain Consider the transformation defined by y n = L x n If L is a linear system, it will satisfy the principle of superposition L x 1 n + x 2 n = L x 1 n + L x 2 n By analogy, we define a class of systems that obey a generalized principle of superposition where addition is replaced by convolution H x n = H x 1 n x 2 n = H x 1 n H x 2 n Systems having this property are known as homomorphic systems for convolution, and can be depicted as shown below [Rabiner & Schafer, 1978] Introduction to Speech Processing Ricardo Gutierrez-Osuna 12

13 An important property of homomorphic systems is that they can be represented as a cascade of three homomorphic systems The first system takes inputs combined by convolution and transforms them into an additive combination of the corresponding outputs D x n = D x 1 n x 2 n = D x 1 n + D x 2 n = x 1 n +x 2 n = x n The second system is a conventional linear system that obeys the principle of superposition The third system is the inverse of the first system: it transforms signals combined by addition into signals combined by convolution D 1 y n = D 1 y 1 n + y 2 n = D 1 y 1 n D 1 y 2 n = y 1 n y 2 n = y n This is important because the design of such system reduces to the design of a linear system [Rabiner & Schafer, 1978] Introduction to Speech Processing Ricardo Gutierrez-Osuna 13

14 Noting that the z-transform of two convolved signals is the product of their z-transforms x n = x 1 n x 2 n X z = X 1 z X 2 z We can then take logs to obtain X z = log X z = log X 1 z X 2 z = log X 1 z + log X 2 z Thus, the frequency domain representation of a homomorphic system for deconvolution can be represented as [Rabiner & Schafer, 1978] Introduction to Speech Processing Ricardo Gutierrez-Osuna 14

15 If we wish to represent signals as sequence rather than in the frequency domain, then the systems D and D 1 can be represented as shown below Where you can recognize the similarity between the cepstrum with the system D Strictly speaking, D defines a complex cepstrum, whereas in speech processing we generally use the real cepstrum Can you find the equivalent system for the liftering stage? [Rabiner & Schafer, 1978] Introduction to Speech Processing Ricardo Gutierrez-Osuna 15

16 Voicing/pitch detection Cepstral analysis can be applied to detect local periodicity The figure in the next slide shows the STFT and corresponding spectra for a sequence of analysis windows in a speech signal (50-ms window, 12.5-ms shift) The STFT shows a clear difference in harmonic structure Frames 1-5 correspond to unvoiced speech Frames 8-15 correspond to voiced speech Frames 6-7 contain a mixture of voiced and unvoiced excitation These differences are perhaps more apparent in the cepstrum, which shows a strong peak at a quefrency of about ms for frames 8-15 Therefore, presence of a strong peak (in the 3-20 ms range) is a very strong indication that the speech segment is voiced Lack of a peak, however, is not an indication of unvoiced speech since the strength or existence of a peak depends on various factors, i.e., length of the analysis window Introduction to Speech Processing Ricardo Gutierrez-Osuna 16

17 [Rabiner & Schafer, 2007] Introduction to Speech Processing Ricardo Gutierrez-Osuna 17

18 Cepstral-based parameterizations Linear prediction cepstral coefficients As we saw, the cepstrum has a number of advantages (source-filter separation, compactness, orthogonality), whereas the LP coefficients are too sensitive to numerical precision Thus, it is often desirable to transform LP coefficients a n into cepstral coefficients c n This can be achieved as [Huang, Acero & Hon, 2001] ln G n = 0 c n = a n + 1 n 1 kc n k a n k 1 < n p k=1 Introduction to Speech Processing Ricardo Gutierrez-Osuna 18

19 Mel Frequency Cepstral Coefficients (MFCC) Probably the most common parameterization in speech recognition Combines the advantages of the cepstrum with a frequency scale based on critical bands Computing MFCCs First, the speech signal is analyzed with the STFT Then, DFT values are grouped together in critical bands and weighted according to the triangular weighting function shown below These bandwidths are constant for center frequencies below 1KHz and increase exponentially up to half the sampling rate [Rabiner & Schafer, 2007] Introduction to Speech Processing Ricardo Gutierrez-Osuna 19

20 The mel-frequency spectrum at analysis time n is defined as U r MF r = 1 V A r k X n, k r k=l r where V r k is the triangular weighting function for the r-th filter, ranging from DFT index L r to U r and U r A r = V r k 2 k=l r which serves as a normalization factor for the r-th filter, so that a perfectly flat Fourier spectrum will also produce a flat Mel-spectrum For each frame, a discrete cosine transform (DCT) of the logmagnitude of the filter outputs is then computed to obtain the MFCCs R MFCC m = 1 log MF r cos 2π R R r m r=1 where typically MFCC m is evaluated for a number of coefficients N MFCC that is less than the number of mel-filters R For F s = 8KHz, typical values are N MFCC = 13 and R = 22 Introduction to Speech Processing Ricardo Gutierrez-Osuna 20

21 Comparison of smoothing techniques: LPC, cepstral and mel-cepstral [Rabiner & Schafer, 2007] Introduction to Speech Processing Ricardo Gutierrez-Osuna 21

22 Notes The MFCC is no longer a homomorphic transformation It would be if the order of summation and logarithms were reversed, in other words if we computed Instead of 1 A r log 1 A r U r k=l r log V r k X n, k U r V r k X n, k k=l r In practice, however, the MFCC representation is approximately homomorphic for filters that have a smooth transfer function The advantages of the second summation above is that the filter energies are more robust to noise and spectral properties Introduction to Speech Processing Ricardo Gutierrez-Osuna 22

23 MFCCs employ the DCT instead of the IDFT The DCT turns out to be closely related to the Karhunen-Loeve transform The KL transform is the basis for PCA, a technique that can be used to find orthogonal (uncorrelated) projections of high dimensional data As a result, the DCT tends to decorrelate the mel-scale frequency log-energies Relationship with the DFT The DCT transform (the DCT-II, to be exact) is defined as N 1 X DCT k = x n cos π N n k n=0 whereas the DFT is defined as X DFT k = N 1 x n e j2π N kn n=0 Under some conditions, the DFT and the DCT-II are exactly equivalent For our purposes, you can think of the DCT as a real version of the DFT (i.e., no imaginary part) that also has a better energy compaction properties than the DFT Introduction to Speech Processing Ricardo Gutierrez-Osuna 23

24 ex9p2.m Computing MFCCs Introduction to Speech Processing Ricardo Gutierrez-Osuna 24

### MUSICAL INSTRUMENT FAMILY CLASSIFICATION

MUSICAL INSTRUMENT FAMILY CLASSIFICATION Ricardo A. Garcia Media Lab, Massachusetts Institute of Technology 0 Ames Street Room E5-40, Cambridge, MA 039 USA PH: 67-53-0 FAX: 67-58-664 e-mail: rago @ media.

### Speech Analysis for Automatic Speech Recognition

Speech Analysis for Automatic Speech Recognition Noelia Alcaraz Meseguer Master of Science in Electronics Submission date: July 2009 Supervisor: Torbjørn Svendsen, IET Norwegian University of Science and

### T-61.184. Automatic Speech Recognition: From Theory to Practice

Automatic Speech Recognition: From Theory to Practice http://www.cis.hut.fi/opinnot// September 27, 2004 Prof. Bryan Pellom Department of Computer Science Center for Spoken Language Research University

### Lecture 1-10: Spectrograms

Lecture 1-10: Spectrograms Overview 1. Spectra of dynamic signals: like many real world signals, speech changes in quality with time. But so far the only spectral analysis we have performed has assumed

### Available from Deakin Research Online:

This is the authors final peered reviewed (post print) version of the item published as: Adibi,S 2014, A low overhead scaled equalized harmonic-based voice authentication system, Telematics and informatics,

### 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.

### Short-time FFT, Multi-taper analysis & Filtering in SPM12

Short-time FFT, Multi-taper analysis & Filtering in SPM12 Computational Psychiatry Seminar, FS 2015 Daniel Renz, Translational Neuromodeling Unit, ETHZ & UZH 20.03.2015 Overview Refresher Short-time Fourier

### 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

### Artificial Neural Network for Speech Recognition

Artificial Neural Network for Speech Recognition Austin Marshall March 3, 2005 2nd Annual Student Research Showcase Overview Presenting an Artificial Neural Network to recognize and classify speech Spoken

### SGN-1158 Introduction to Signal Processing Test. Solutions

SGN-1158 Introduction to Signal Processing Test. Solutions 1. Convolve the function ( ) with itself and show that the Fourier transform of the result is the square of the Fourier transform of ( ). (Hints:

### 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,

### 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

### Automatic Evaluation Software for Contact Centre Agents voice Handling Performance

International Journal of Scientific and Research Publications, Volume 5, Issue 1, January 2015 1 Automatic Evaluation Software for Contact Centre Agents voice Handling Performance K.K.A. Nipuni N. Perera,

### SIGNAL PROCESSING & SIMULATION NEWSLETTER

1 of 10 1/25/2008 3:38 AM SIGNAL PROCESSING & SIMULATION NEWSLETTER Note: This is not a particularly interesting topic for anyone other than those who ar e involved in simulation. So if you have difficulty

### Lecture 1-6: Noise and Filters

Lecture 1-6: Noise and Filters Overview 1. Periodic and Aperiodic Signals Review: by periodic signals, we mean signals that have a waveform shape that repeats. The time taken for the waveform to repeat

### Analysis/resynthesis with the short time Fourier transform

Analysis/resynthesis with the short time Fourier transform summer 2006 lecture on analysis, modeling and transformation of audio signals Axel Röbel Institute of communication science TU-Berlin IRCAM Analysis/Synthesis

### 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

### Advanced Signal Processing and Digital Noise Reduction

Advanced Signal Processing and Digital Noise Reduction Saeed V. Vaseghi Queen's University of Belfast UK WILEY HTEUBNER A Partnership between John Wiley & Sons and B. G. Teubner Publishers Chichester New

### 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

### Applications of the DFT

CHAPTER 9 Applications of the DFT The Discrete Fourier Transform (DFT) is one of the most important tools in Digital Signal Processing. This chapter discusses three common ways it is used. First, the DFT

### Emotion Detection from Speech

Emotion Detection from Speech 1. Introduction Although emotion detection from speech is a relatively new field of research, it has many potential applications. In human-computer or human-human interaction

### ANALYZER BASICS WHAT IS AN FFT SPECTRUM ANALYZER? 2-1

WHAT IS AN FFT SPECTRUM ANALYZER? ANALYZER BASICS The SR760 FFT Spectrum Analyzer takes a time varying input signal, like you would see on an oscilloscope trace, and computes its frequency spectrum. Fourier's

### 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

### Quarterly Progress and Status Report. Measuring inharmonicity through pitch extraction

Dept. for Speech, Music and Hearing Quarterly Progress and Status Report Measuring inharmonicity through pitch extraction Galembo, A. and Askenfelt, A. journal: STL-QPSR volume: 35 number: 1 year: 1994

### 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

### HD Radio FM Transmission System Specifications Rev. F August 24, 2011

HD Radio FM Transmission System Specifications Rev. F August 24, 2011 SY_SSS_1026s TRADEMARKS HD Radio and the HD, HD Radio, and Arc logos are proprietary trademarks of ibiquity Digital Corporation. ibiquity,

### Analog and Digital Signals, Time and Frequency Representation of Signals

1 Analog and Digital Signals, Time and Frequency Representation of Signals Required reading: Garcia 3.1, 3.2 CSE 3213, Fall 2010 Instructor: N. Vlajic 2 Data vs. Signal Analog vs. Digital Analog Signals

### AN-007 APPLICATION NOTE MEASURING MAXIMUM SUBWOOFER OUTPUT ACCORDING ANSI/CEA-2010 STANDARD INTRODUCTION CEA-2010 (ANSI) TEST PROCEDURE

AUDIOMATICA AN-007 APPLICATION NOTE MEASURING MAXIMUM SUBWOOFER OUTPUT ACCORDING ANSI/CEA-2010 STANDARD by Daniele Ponteggia - dp@audiomatica.com INTRODUCTION The Consumer Electronics Association (CEA),

### The Fourier Analysis Tool in Microsoft Excel

The Fourier Analysis Tool in Microsoft Excel Douglas A. Kerr Issue March 4, 2009 ABSTRACT AD ITRODUCTIO The spreadsheet application Microsoft Excel includes a tool that will calculate the discrete Fourier

### Frequency Response of FIR Filters

Frequency Response of FIR Filters Chapter 6 This chapter continues the study of FIR filters from Chapter 5, but the emphasis is frequency response, which relates to how the filter responds to an input

### Spectrum Level and Band Level

Spectrum Level and Band Level ntensity, ntensity Level, and ntensity Spectrum Level As a review, earlier we talked about the intensity of a sound wave. We related the intensity of a sound wave to the acoustic

### Introduction to Digital Filters

CHAPTER 14 Introduction to Digital Filters Digital filters are used for two general purposes: (1) separation of signals that have been combined, and (2) restoration of signals that have been distorted

### The Algorithms of Speech Recognition, Programming and Simulating in MATLAB

FACULTY OF ENGINEERING AND SUSTAINABLE DEVELOPMENT. The Algorithms of Speech Recognition, Programming and Simulating in MATLAB Tingxiao Yang January 2012 Bachelor s Thesis in Electronics Bachelor s Program

### Lecture 14. Point Spread Function (PSF)

Lecture 14 Point Spread Function (PSF), Modulation Transfer Function (MTF), Signal-to-noise Ratio (SNR), Contrast-to-noise Ratio (CNR), and Receiver Operating Curves (ROC) Point Spread Function (PSF) Recollect

### 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

### Separation and Classification of Harmonic Sounds for Singing Voice Detection

Separation and Classification of Harmonic Sounds for Singing Voice Detection Martín Rocamora and Alvaro Pardo Institute of Electrical Engineering - School of Engineering Universidad de la República, Uruguay

### 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

### 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

### Digital Speech Coding

Digital Speech Processing David Tipper Associate Professor Graduate Program of Telecommunications and Networking University of Pittsburgh Telcom 2720 Slides 7 http://www.sis.pitt.edu/~dtipper/tipper.html

### Lab 1. The Fourier Transform

Lab 1. The Fourier Transform Introduction In the Communication Labs you will be given the opportunity to apply the theory learned in Communication Systems. Since this is your first time to work in the

### PeakVue Analysis for Antifriction Bearing Fault Detection

August 2011 PeakVue Analysis for Antifriction Bearing Fault Detection Peak values (PeakVue) are observed over sequential discrete time intervals, captured, and analyzed. The analyses are the (a) peak values

### Convolution, Correlation, & Fourier Transforms. James R. Graham 10/25/2005

Convolution, Correlation, & Fourier Transforms James R. Graham 10/25/2005 Introduction A large class of signal processing techniques fall under the category of Fourier transform methods These methods fall

### BLIND SOURCE SEPARATION OF SPEECH AND BACKGROUND MUSIC FOR IMPROVED SPEECH RECOGNITION

BLIND SOURCE SEPARATION OF SPEECH AND BACKGROUND MUSIC FOR IMPROVED SPEECH RECOGNITION P. Vanroose Katholieke Universiteit Leuven, div. ESAT/PSI Kasteelpark Arenberg 10, B 3001 Heverlee, Belgium Peter.Vanroose@esat.kuleuven.ac.be

### 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

### MFCC-Based Voice Recognition System for Home Automation Using Dynamic Programming

International Journal of Science and Research (IJSR) MFCC-Based Voice Recognition System for Home Automation Using Dynamic Programming Sandeep Joshi1, Sneha Nagar2 1 PG Student, Embedded Systems, Oriental

### Appendix D Digital Modulation and GMSK

D1 Appendix D Digital Modulation and GMSK A brief introduction to digital modulation schemes is given, showing the logical development of GMSK from simpler schemes. GMSK is of interest since it is used

### SHOCK AND VIBRATION RESPONSE SPECTRA COURSE Unit 3. Sine Sweep Frequency and Octave Calculations

SHOCK AND VIBRATION RESPONSE SPECTRA COURSE Unit 3. Sine Sweep Frequency and Octave Calculations By Tom Irvine Introduction A common specification for a base excitation test is a sine sweep test. An example

### Trigonometric functions and sound

Trigonometric functions and sound The sounds we hear are caused by vibrations that send pressure waves through the air. Our ears respond to these pressure waves and signal the brain about their amplitude

### Myanmar Continuous Speech Recognition System Based on DTW and HMM

Myanmar Continuous Speech Recognition System Based on DTW and HMM Ingyin Khaing Department of Information and Technology University of Technology (Yatanarpon Cyber City),near Pyin Oo Lwin, Myanmar Abstract-

### Wavelet analysis. Wavelet requirements. Example signals. Stationary signal 2 Hz + 10 Hz + 20Hz. Zero mean, oscillatory (wave) Fast decay (let)

Wavelet analysis In the case of Fourier series, the orthonormal basis is generated by integral dilation of a single function e jx Every 2π-periodic square-integrable function is generated by a superposition

### RANDOM VIBRATION AN OVERVIEW by Barry Controls, Hopkinton, MA

RANDOM VIBRATION AN OVERVIEW by Barry Controls, Hopkinton, MA ABSTRACT Random vibration is becoming increasingly recognized as the most realistic method of simulating the dynamic environment of military

### Jeff Thomas Tom Holmes Terri Hightower. Learn RF Spectrum Analysis Basics

Jeff Thomas Tom Holmes Terri Hightower Learn RF Spectrum Analysis Basics Learning Objectives Name the major measurement strengths of a swept-tuned spectrum analyzer Explain the importance of frequency

### Design of FIR Filters

Design of FIR Filters Elena Punskaya www-sigproc.eng.cam.ac.uk/~op205 Some material adapted from courses by Prof. Simon Godsill, Dr. Arnaud Doucet, Dr. Malcolm Macleod and Prof. Peter Rayner 68 FIR as

### 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,

### 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

### WAVEFORM DICTIONARIES AS APPLIED TO THE AUSTRALIAN EXCHANGE RATE

Sunway Academic Journal 3, 87 98 (26) WAVEFORM DICTIONARIES AS APPLIED TO THE AUSTRALIAN EXCHANGE RATE SHIRLEY WONG a RAY ANDERSON Victoria University, Footscray Park Campus, Australia ABSTRACT This paper

### 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

### SR2000 FREQUENCY MONITOR

SR2000 FREQUENCY MONITOR THE FFT SEARCH FUNCTION IN DETAILS FFT Search is a signal search using FFT (Fast Fourier Transform) technology. The FFT search function first appeared with the SR2000 Frequency

### CIRCUITS LABORATORY EXPERIMENT 3. AC Circuit Analysis

CIRCUITS LABORATORY EXPERIMENT 3 AC Circuit Analysis 3.1 Introduction The steady-state behavior of circuits energized by sinusoidal sources is an important area of study for several reasons. First, the

### Basic Acoustics and Acoustic Filters

Basic CHAPTER Acoustics and Acoustic Filters 1 3 Basic Acoustics and Acoustic Filters 1.1 The sensation of sound Several types of events in the world produce the sensation of sound. Examples include doors

### TCOM 370 NOTES 99-4 BANDWIDTH, FREQUENCY RESPONSE, AND CAPACITY OF COMMUNICATION LINKS

TCOM 370 NOTES 99-4 BANDWIDTH, FREQUENCY RESPONSE, AND CAPACITY OF COMMUNICATION LINKS 1. Bandwidth: The bandwidth of a communication link, or in general any system, was loosely defined as the width of

### VCO Phase noise. Characterizing Phase Noise

VCO Phase noise Characterizing Phase Noise The term phase noise is widely used for describing short term random frequency fluctuations of a signal. Frequency stability is a measure of the degree to which

### Sampling and Interpolation. Yao Wang Polytechnic University, Brooklyn, NY11201

Sampling and Interpolation Yao Wang Polytechnic University, Brooklyn, NY1121 http://eeweb.poly.edu/~yao Outline Basics of sampling and quantization A/D and D/A converters Sampling Nyquist sampling theorem

### SPEAKER IDENTIFICATION FROM YOUTUBE OBTAINED DATA

SPEAKER IDENTIFICATION FROM YOUTUBE OBTAINED DATA Nitesh Kumar Chaudhary 1 and Shraddha Srivastav 2 1 Department of Electronics & Communication Engineering, LNMIIT, Jaipur, India 2 Bharti School Of Telecommunication,

### MPEG Unified Speech and Audio Coding Enabling Efficient Coding of both Speech and Music

ISO/IEC MPEG USAC Unified Speech and Audio Coding MPEG Unified Speech and Audio Coding Enabling Efficient Coding of both Speech and Music The standardization of MPEG USAC in ISO/IEC is now in its final

### Introduction to Digital Audio

Introduction to Digital Audio Before the development of high-speed, low-cost digital computers and analog-to-digital conversion circuits, all recording and manipulation of sound was done using analog techniques.

### (Refer Slide Time: 01:11-01:27)

Digital Signal Processing Prof. S. C. Dutta Roy Department of Electrical Engineering Indian Institute of Technology, Delhi Lecture - 6 Digital systems (contd.); inverse systems, stability, FIR and IIR,

### Establishing the Uniqueness of the Human Voice for Security Applications

Proceedings of Student/Faculty Research Day, CSIS, Pace University, May 7th, 2004 Establishing the Uniqueness of the Human Voice for Security Applications Naresh P. Trilok, Sung-Hyuk Cha, and Charles C.

### The continuous and discrete Fourier transforms

FYSA21 Mathematical Tools in Science The continuous and discrete Fourier transforms Lennart Lindegren Lund Observatory (Department of Astronomy, Lund University) 1 The continuous Fourier transform 1.1

AM Receiver Prelab In this experiment you will use what you learned in your previous lab sessions to make an AM receiver circuit. You will construct an envelope detector AM receiver. P1) Introduction One

### Using the Impedance Method

Using the Impedance Method The impedance method allows us to completely eliminate the differential equation approach for the determination of the response of circuits. In fact the impedance method even

### A TOOL FOR TEACHING LINEAR PREDICTIVE CODING

A TOOL FOR TEACHING LINEAR PREDICTIVE CODING Branislav Gerazov 1, Venceslav Kafedziski 2, Goce Shutinoski 1 1) Department of Electronics, 2) Department of Telecommunications Faculty of Electrical Engineering

### Music Genre Classification

Music Genre Classification Michael Haggblade Yang Hong Kenny Kao 1 Introduction Music classification is an interesting problem with many applications, from Drinkify (a program that generates cocktails

### 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;

### 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

### CHAPTER 6 Frequency Response, Bode Plots, and Resonance

ELECTRICAL CHAPTER 6 Frequency Response, Bode Plots, and Resonance 1. State the fundamental concepts of Fourier analysis. 2. Determine the output of a filter for a given input consisting of sinusoidal

### Marathi Interactive Voice Response System (IVRS) using MFCC and DTW

Marathi Interactive Voice Response System (IVRS) using MFCC and DTW Manasi Ram Baheti Department of CSIT, Dr.B.A.M. University, Aurangabad, (M.S.), India Bharti W. Gawali Department of CSIT, Dr.B.A.M.University,

### 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

### Doppler. Doppler. Doppler shift. Doppler Frequency. Doppler shift. Doppler shift. Chapter 19

Doppler Doppler Chapter 19 A moving train with a trumpet player holding the same tone for a very long time travels from your left to your right. The tone changes relative the motion of you (receiver) and

### Electronic Communications Committee (ECC) within the European Conference of Postal and Telecommunications Administrations (CEPT)

Page 1 Electronic Communications Committee (ECC) within the European Conference of Postal and Telecommunications Administrations (CEPT) ECC RECOMMENDATION (06)01 Bandwidth measurements using FFT techniques

### Lecture 7 ELE 301: Signals and Systems

Lecture 7 ELE 3: Signals and Systems Prof. Paul Cuff Princeton University Fall 2-2 Cuff (Lecture 7) ELE 3: Signals and Systems Fall 2-2 / 22 Introduction to Fourier Transforms Fourier transform as a limit

### TCOM 370 NOTES 99-6 VOICE DIGITIZATION AND VOICE/DATA INTEGRATION

TCOM 370 NOTES 99-6 VOICE DIGITIZATION AND VOICE/DATA INTEGRATION (Please read appropriate parts of Section 2.5.2 in book) 1. VOICE DIGITIZATION IN THE PSTN The frequencies contained in telephone-quality

### Appendix C GSM System and Modulation Description

C1 Appendix C GSM System and Modulation Description C1. Parameters included in the modelling In the modelling the number of mobiles and their positioning with respect to the wired device needs to be taken

### Security in Voice Authentication

Security in Voice Authentication by Chenguang Yang A Dissertation Submitted to the Faculty of the WORCESTER POLYTECHNIC INSTITUTE In partial fulfillment of the requirements for the Degree of Doctor of

### 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

### 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

### Convention Paper Presented at the 135th Convention 2013 October 17 20 New York, USA

Audio Engineering Society Convention Paper Presented at the 135th Convention 2013 October 17 20 New York, USA This Convention paper was selected based on a submitted abstract and 750-word precis that have

### The Calculation of G rms

The Calculation of G rms QualMark Corp. Neill Doertenbach The metric of G rms is typically used to specify and compare the energy in repetitive shock vibration systems. However, the method of arriving

ECSE 4440 Control System Engineering Fall 2001 Project 3 Controller Design in Frequency Domain TA 1. Abstract 2. Introduction 3. Controller design in Frequency domain 4. Experiment 5. Colclusion 1. Abstract

### Application Note: Spread Spectrum Oscillators Reduce EMI for High Speed Digital Systems

Application Note: Spread Spectrum Oscillators Reduce EMI for High Speed Digital Systems Introduction to Electro-magnetic Interference Design engineers seek to minimize harmful interference between components,

### Laboratory Manual and Supplementary Notes. CoE 494: Communication Laboratory. Version 1.2

Laboratory Manual and Supplementary Notes CoE 494: Communication Laboratory Version 1.2 Dr. Joseph Frank Dr. Sol Rosenstark Department of Electrical and Computer Engineering New Jersey Institute of Technology

### FEGYVERNEKI SÁNDOR, PROBABILITY THEORY AND MATHEmATICAL

FEGYVERNEKI SÁNDOR, PROBABILITY THEORY AND MATHEmATICAL STATIsTICs 4 IV. RANDOm VECTORs 1. JOINTLY DIsTRIBUTED RANDOm VARIABLEs If are two rom variables defined on the same sample space we define the joint

### 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

### T = 1 f. Phase. Measure of relative position in time within a single period of a signal For a periodic signal f(t), phase is fractional part t p

Data Transmission Concepts and terminology Transmission terminology Transmission from transmitter to receiver goes over some transmission medium using electromagnetic waves Guided media. Waves are guided

### A Digital Audio Watermark Embedding Algorithm

Xianghong Tang, Yamei Niu, Hengli Yue, Zhongke Yin Xianghong Tang, Yamei Niu, Hengli Yue, Zhongke Yin School of Communication Engineering, Hangzhou Dianzi University, Hangzhou, Zhejiang, 3008, China tangxh@hziee.edu.cn,

### CBS RECORDS PROFESSIONAL SERIES CBS RECORDS CD-1 STANDARD TEST DISC

CBS RECORDS PROFESSIONAL SERIES CBS RECORDS CD-1 STANDARD TEST DISC 1. INTRODUCTION The CBS Records CD-1 Test Disc is a highly accurate signal source specifically designed for those interested in making

### 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.