The Channel Vocoder (analyzer):

Save this PDF as:
 WORD  PNG  TXT  JPG

Size: px
Start display at page:

Download "The Channel Vocoder (analyzer):"

Transcription

1 Vocoders 1

2 The Channel Vocoder (analyzer): The channel vocoder employs a bank of bandpass filters, Each having a bandwidth between 100 Hz and 300 Hz. Typically, linear phase FIR filter are used. The output of each filter is rectified and lowpass filtered. The bandwidth of the lowpass filter is selected to match the time variations in the characteristics of the vocal tract. For measurement of the spectral magnitudes, a voicing detector and a pitch estimator are included in the speech analysis. 2

3 The Channel Vocoder (analyzer block diagram): Bandpass Filter Rectifier Lowpass Filter A/D Converter S(n) Bandpass Filter Rectifier Lowpass Filter A/D Converter Encoder To Channel Voicing detector Pitch detector 3

4 The Channel Vocoder (synthesizer): linear-phase FIR filters Covering 0-4 khz Each having a bandwidth between Hz 20-ms frames, or 50 Hz changing of spectral magnitude LPF bandwidth: Hz Sampling rate of the output of the filters: 50 Hz 4

5 The Channel Vocoder (synthesizer): Bit rate: 1 bit for voicing detector 6 bits for pitch period For 16 channels, each coded with 3-4 bits, updated 50 times per second Then the total bit rate is bps Further reductions to 1200 bps can be achieved by exploiting frequency correlations of the spectrum magnitude 5

6 The Channel Vocoder (synthesizer): At the receiver the signal samples are passed through D/A converters. The outputs of the D/As are multiplied by the voiced or unvoiced signal sources. The resulting signal are passed through bandpass filters. The outputs of the bandpass filters are summed to form the synthesized speech signal. 6

7 The Channel Vocoder (synthesizer block diagram): D/A Converter Bandpass Filter Output speech From Channel Decoder D/A Converter Voicing Information Bandpass Filter Switch Pitch period Pulse generator Random Noise generator 7

8 The Phase Vocoder : The phase vocoder is similar to the channel vocoder. However, instead of estimating the pitch, the phase vocoder estimates the phase derivative at the output of each filter. By coding and transmitting the phase derivative, this vocoder destroys the phase information. 8

9 The Phase Vocoder (analyzer block diagram, kth channel) S(n) cos k n Lowpass Filter cos k n a k n Differentiator Differentiator Compute Short-term Magnitude And Phase Derivative Short-term magnitude sin k n Decimator Encoder To Channel sin k n Lowpass Filter cos k n b k n Decimator Short-term phase derivative 9

10 The Phase Vocoder (synthesizer block diagram, kth channel) Decimated Short-term amplitude cos k n From Channel Decoder Integrator Cos Interpolator Decimated Sin Interpolator Short-term Phase sin k n derivative 10

11 The Phase Vocoder : LPF bandwidth: 50 Hz Demodulation separation: 100 Hz Number of filters: Sampling rate of spectrum magnitude and phase derivative: samples per second Spectral magnitude is coded using PCM or DPCM Phase derivative is coded linearly using 2-3 bits The resulting bit rate is 7200 bps 11

12 The Formant Vocoder : The formant vocoder can be viewed as a type of channel vocoder that estimates the first three or four formants in a segment of speech. It is this information plus the pitch period that is encoded and transmitted to the receiver. 12

13 The Formant Vocoder : Example of formant: (a) : The spectrogram of the utterance day one showing the pitch and the harmonic structure of speech. (b) : A zoomed spectrogram of the fundamental and the second harmonic. (a) (b) 13

14 The Formant Vocoder (analyzer block diagram): Input Speech F3 F2 F1 F3 B3 F2 B2 F1 B1 Pitch And V/U Decoder V/U F0 Fk :The frequency of the kth formant Bk :The bandwidth of the kth formant 14

15 The Formant Vocoder (synthesizer block diagram): F3 B3 F2 B2 F1 B1 V/U F0 F3 F2 F1 Excitation Signal 15

16 Linear Predictive Coding : The objective of LP analysis is to estimate parameters of an all-pole model for the vocal tract. Several methods have been devised for generating the excitation sequence for speech synthesizes. Various LPC-type speech analysis and synthesis methods differ primarily in the type of excitation signal generated for speech synthesis. 16

17 LPC 10 : This methods is called LPC-10 because of 10 coefficient are typically employed. LPC-10 partitions the speech into the 180 sample frame. Pitch and voicing decision are determined by using the AMDF and zero crossing measures. 17

18 A General Discrete-Time Model For Speech Production Pitch Gain s(n) Voiced DT Impulse generator G(z) Glottal Filter U(n) Voiced Volume velocity V U H(z) Vocal tract Filter R(z) LP Filter Speech Signal Unvoiced Uncorrelated Noise generator Gain 18

19 پيشگويي خطي تعيين مرتبه پيشگويي صفحه 19 از 54

20 پيشگويي خطي تعيين مرتبه پيشگويي صفحه 20 از 54

21 پيشگويي خطي تعيين مرتبه پيشگويي PG 10log m n m M 1 m n m M 1 s e 2 2 [ n] [ n] صفحه 21 از 54

22 پيشگويي خطي مثال M=4 M=10 صفحه 22 از 54

23 پيشگويي خطي مثال M=2 M=10 M=54 صفحه 23 از 54

24 پيشگويي خطي ايده پيشگويي خطي بلند مدت M=10 M=50 صفحه 24 از 54

25 پيشگويي خطي پيشگويي خطي بلند مدت صفحه 25 از 54

26 وكدر LPC10 مشخصات عمومي LPC10 صفحه 26

27 كد كننده وكدر LPC10 PCM LPC LPC LPC Bit Encoder صفحه 27 از 54

28 28 هحفص چيپ دويرپ صيخشت YMC m N m n l] s[n]s[n R[l,m] 1 m N m n l n s n s m l MDF 1 ] [ ] [ ], [ m N m n e N n s b n s 1 ], [ ] [. ] [

29 وكدر LPC10 MDF T=20,21,,39,40,42,,80,84,,154 صفحه 29 از 54

30 وكدر LPC10 كد كننده LPC RC صفحه 30 از 54

31 وكدر LPC10 سنتز گفتار سيگنال اصلي بخش كد كننده تعيين صدادار/بيصدا بودن فريم تعيين دوره گام فثط براي حالت صدادار محاسبه بهره سيگنال V/U قطار ضربه با پريود يراير دوره گام G گفتار سنتز شده نويز تصادفي صفحه 31

32 وكدر LPC10 محدوديتها AR صفحه 32

33 Residual Excited LP Vocoder : Speech quality can be improved at the expense of a higher bit rate by computing and transmitting a residual error, as done in the case of DPCM. One method is that the LPC model and excitation parameters are estimated from a frame of speech. 33

34 Residual Excited LP Vocoder : The speech is synthesized at the transmitter and subtracted from the original speech signal to form the residual error. The residual error is quantized, coded, and transmitted to the receiver At the receiver the signal is synthesized by adding the residual error to the signal generated from the model. 34

35 Residual Excited LP Vocoder : The residual signal is low-pass filtered at 1000 Hz in the analyzer to reduce bit rate In the synthesizer, it is rectified and spectrum flattened (using a HPF), the lowpass and highpass signals are summed and the resulting residual error signal is used to excite the LPC model. RELP vocoder provides communication-quality speech at about 9600 bps. 35

36 RELP Analyzer (type 1): S(n) Buffer And window f (n; m) e (n; m) Residual error Excitation parameters stlp analysis Θˆ 0, gain estimate V/U, decision Pˆ, pitch estimate LP Parameters {â(i;m)} LP Synthesis model Encoder To Channel 36

37 RELP Analyzer (type 2): S(n) Buffer f (n; m) Inverse And Filter window Â(z;m) Prediction Residual (n;m) Lowpass Filter Decimator DFT Encoder To Channel stlp analysis LP Parameters {â(i;m)} 37

38 Synthesizer for a RELP vocoder From Channel Decoder Buffer And Controller Residual Interpolator Rectifier Highpass Filter LP model Parameter updates LP synthesizer Excitation 38

39 Multipulse LPC Vocoder RELP needs to regenerate the highfrequency components at the decoder. A crude approximation of the high frequencies The multipulse LPC is a time domain analysis-by-synthesis method that results in a better excitation signal for the LPC vocal system filter. 39

40 Multipulse LPC Vocoder The information concerning the excitation sequence includes: the location of the pulses an overall scale factor corresponding to the largest pulse amplitude The pulse amplitudes relative to the overall scale factor The scale factor is logarithmically quantized into 6 bits. The amplitudes are linearly quantized into 4 bits. The pulse locations are encoded using a differential coding scheme. The excitation parameters are updated every 5 msec. The LPC vocal-tract parameters and the pitch period are updated every 20 msec. The bit rate is 9600 bps. 40

41 Analysis-by-synthesis coder A stored sequence from a Gaussian excitation codebook is scaled and used to excite the cascade of a pitch synthesis filter and the LPC synthesis filter The synthetic speech is compared with the original speech Residual error signal is weighted perceptually by a filter ˆ( z / c) W ( z) ˆ( z) Aˆ( z) Aˆ( z / c) 41

42 Obtaining the multipulse excitation: (Analysis by synthesis method) Input speech s(n) Pˆ Buffer And LP analysis Pitch Synthesis filterθ p (z) LP Synthesis filter - fˆ(n;m) f(n;m) + (n;m) Perceptual Weighting filter W(z) Multipulse Excitation generator Error minimization W (n;m) 42

43 Code Excited LP : CELP is an analysis-by-synthesis method in which the excitation sequence is selected from a codebook of zero-mean Gaussian sequence. The bit rate of the CELP is 4800 bps. 43

44 CELP (analysis-by-synthesis coder) : Speech samples Gaussian Excitation codebook Gain Pitch Synthesis filter LP parameters Spectral Envelope (LP) Synthesis filter Buffer and LP analysis Side information Perceptual Weighting Filter W(z) Computer Energy (square and sum) Index of Excitation sequence 44

45 Analysis-by-synthesis coder This weighted error is squared and summed over a subframe block to give the error energy By performing an exhaustive search through the codebook we find the excitation sequence that minimize the error energy 45

46 Analysis-by-synthesis coder The gain factor for scaling the excitation sequence is determined for each codeword in the codebook by minimizing the error energy for the block of samples 46

47 CELP (synthesizer) : From Channel decoder Buffer And controller Gaussian Excitation codebook Pitch Synthesis filter LP Synthesis filter LP parameters, gain and pitch estimate updates 47

48 CELP synthesizer Cascade of two all-pole filter with coefficients that are updated periodically First filter is a long-delay pitch filter used to generate the pitch periodicity in voiced speech This filter has this form p ( z) p 1 bz p 48

49 CELP Parameters of the filter can be determined by minimizing the prediction error energy, after pitch estimation,over a frame duration of 5msec Second filter is a short-delay all-pole (vocal-tract) filter and has coefficients that are determined every 10-20msec 49

50 Example: sampling frequency is 8khz subframe block duration for the pitch estimation and excitation sequence is performed every 5msec. We have 40 samples per 5-msec The excitation sequence consist of 40 samples 50

51 Example: A codebook of 1024 sequences gives good-quality speech For such codebook size,we require 10bits to send codebook index Hence the bit rate is reduced by a factor of 4 The transmission of pitch predictor parameters and spectral predictor brings the bit rate to about 4800 bps 51

52 Low-delay CELP coder CELP has been used to achieve tollquality speech at bps with low delay. Although other types of vocoders produces high quality speech at bps these vocoders buffer 10-20msec of speech samples 52

53 Low-delay CELP coder The one way delay is of the order of msec With modification of CELP, it is possible to reduce the one-way delay to about 2ms Low-delay CELP is achieved by using a backward-adaptive predictor with a gain parameter and an excitation vector size as small as 5 samples 53

54 Low-delay CELP coder Input Speech s(n) Buffer and window Excitation Vector quantizer codebook Gain LP (high-order) Synthesis filter fˆ(n;m) f(n;m) + - (n;m) Gain adaptation Predictor adaptation Perceptual Weighting Filter W(z) Error minimization W (n;m) 54

55 Low-delay CELP coder Pitch predictor used in the conventional forward-adaptive coder is eliminated In order to compensate for the loss in pitch information, the LPC predictor order is increased significantly, to an order of 50 55

56 Low-delay CELP coder LPC coefficients are updated more frequently, every 2.5 ms 5-sample excitation vector corresponds to an excitation block duration of msec at 8-kHz sampling rate 56

57 Low-delay CELP coder The logarithm of the excitation gain is adapted every subframe excitation block by employing a 10 th -order adaptive linear predictor in the logarithmic scale The coefficients of the logarithmic-gain predictor are updated every four blocks by performing an LPC analysis of previously quantized excitation signal blocks 57

58 Low-delay CELP coder The perceptual weighting filter is also 10 th order and is updated once every four blocks by employing an LPC analysis on frames of the input speech signal of duration 2.5 msec The excitation codebook in the low-delay CELP is also modified compared to conventional CELP 10-bit excitation codebook is employed 58

59 Vector Sum Excited LP : The VSELP coder and decoder basically differ in method by which the excitation sequence is formed In the next block diagram of the VSELP, there are three excitation sources One excitation is obtained from the pitch period state The other two excitation sources are obtained from two codebooks 59

60 VSELP Decoder : Long-term Filter state Codebook 1 0 Pitch synthesis filter Spectral envelop (LP) synthesis filter Spectral post filter Synthetic Speech 1 Codebook

61 VSELP Decoder LPC synthesis filter is implemented as a 10-pole filter and its coefficients are coded and transmitted every 20ms Coefficients are updated in each 5-ms frame by interpolation Excitation parameters are also updated every 5ms 61

62 VSELP Decoder 128 codewords in each of the two codebooks codewords are constructed from two sets of seven basis codewords by forming linear combinations of the seven basis codewords The long-term filter state is also a codebook with 128 codeword sequences 62

63 VSELP Decoder In each 5-msec frame, the codewords from this codebook are filtered through the speech system filter and correlated with the input speech sequence ˆ ( z ) The filtered codeword is used to update the history and the lag is transmitted to the decoder 63

64 VSELP Decoder Thus the update occurs by appending the best-filtered codeword to the history codebook The oldest sample in the history array is discarded The result is that the long-term state becomes an adaptive codebook 64

65 VSELP Decoder The three excitation sequences are selected sequentially from each of three codebooks Each codebook search attempts to find the codeword that minimizes the total energy of the perceptually weighted error Once the codewords have been selected the three gain parameters are optimized 65

66 VSELP Decoder Joint gain optimization is sequentially accomplished by orthogonalizing each weighted codeword vectors prior to the codebook search These parameters are vector quantized to one of 256 eight-bit vectors and transmitted in every 5-ms frame 66

67 Vector Sum Excited LP : The bit rate of the VSELP is about 8000 bps. Bit allocations for 8000-bps VSELP Parameters Bits/5-ms Frame Bits/20ms 10 LPC coefficients - 38 Average speech energy - 5 Excitation codewords from two VSELP codebooks Gain parameters 8 32 Lag of pitch filter 7 28 Total

68 VSELP Decoder Finally, an adaptive spectral post filter is employed in VSELP following the LPC synthesis filter; this post filter is a pole-zero filter of the form W ( z) ˆ( z / c) ˆ( z) Aˆ( z) Aˆ( z / c) 68

69 DEMO Speech Codec Male Speaker Female Speaker Music Original Speech/Music (16-bit sampled at 8KHz) FS-1015 (LPC-10e 2.4 kb/s) FS-1016(CELP 4.8 kb/s) IS-54 ( VSELP 7.95 kb/s) G.721 (32 kb/s ADPCM) 69

70 Standard Voice Algorithms G.711 The most widely used digital representation of voice signals is that of the G.711 or PCM (Pulse Code Modulation) This codec represents a 4 khz band limited voice signal sampled at 8 khz using 8 bits per sample A-law or m-law coding. G.726 The protocol for the G.726 codec requires a 64 kbps A-Law or m-law PCM signal to be encoded into four different bit rate options ranging from 2 bits per sample to 5 bits per sample The algorithm is based on Adaptive Differential Pulse Code Modulation (ADPCM) and is based on 1 sample backward prediction scheme. 70

71 G.728 The G.728 algorithm compresses PCM codec voice signals to a bit rate of 16 kbps. This algorithm is based on a strong backward prediction scheme and is by far considered as one of the most complex voice algorithms to be produced by the ITU standard organization. G.729 For compression of voice signals at 8 kbps the G.729 algorithm offers toll quality with built in algorithmic delays of less than 15 msec Additional features described in the G.729 Annex ensure VAD1 and Comfort Noise Generation functionalities to enhance the quality and reduce the overall bit rate G The most widely used algorithm for band limited channels, such as VoIP and video conferencing, is that of G The algorithm has two operating bit rates of 6.3 kbps and 5.3 kbps Although the delay is not as low as that of the other ITU standards its quality is near toll quality for the given low bit rates, making it very efficient in bit usage. 71

72 GSM2 AMR The latest GSM standard is the multi rate Adaptive Code Excited Linear Prediction that provides compression in the range of 4.75 to 12.2 kbps In total the codec provides 12 bit rates that cover the half rate to full rate channel capacity. GSM FR The first digital codec used in a mobile environment is the GSM Full Rate vocoder The codec compresses 13 bit PCM sample signals to a rate of 13 kbps The algorithm is based on a very simple Regular Pulse Excited Linear Prediction Coding technique. GSM HR To increase capacity, the GSM committee decided on a lower bit rate of 5.6 kbps for the voice channel The algorithm is based on the Vector Sum Excited Linear Predictive (VSELP) and is computationally as complex as other low bit rate algorithms. 72

Overview of Code Excited Linear Predictive Coder

Overview of Code Excited Linear Predictive Coder Overview of Code Excited Linear Predictive Coder Minal Mulye 1, Sonal Jagtap 2 1 PG Student, 2 Assistant Professor, Department of E&TC, Smt. Kashibai Navale College of Engg, Pune, India Abstract Advances

More information

Digital Speech Processing and Coding

Digital Speech Processing and Coding ENEE408G Spring 2006 Lecture-2 Digital Speech Processing and Coding Spring 06 Instructor: Shihab Shamma Electrical & Computer Engineering University of Maryland, College Park http://www.ece.umd.edu/class/enee408g/

More information

Enhanced Waveform Interpolative Coding at 4 kbps

Enhanced Waveform Interpolative Coding at 4 kbps Enhanced Waveform Interpolative Coding at 4 kbps Oded Gottesman, and Allen Gersho Signal Compression Lab. University of California, Santa Barbara E-mail: [oded, gersho]@scl.ece.ucsb.edu Signal Compression

More information

Speech Coding Technique And Analysis Of Speech Codec Using CS-ACELP

Speech Coding Technique And Analysis Of Speech Codec Using CS-ACELP Speech Coding Technique And Analysis Of Speech Codec Using CS-ACELP Monika S.Yadav Vidarbha Institute of Technology Rashtrasant Tukdoji Maharaj Nagpur University, Nagpur, India monika.yadav@rediffmail.com

More information

Analysis/synthesis coding

Analysis/synthesis coding TSBK06 speech coding p.1/32 Analysis/synthesis coding Many speech coders are based on a principle called analysis/synthesis coding. Instead of coding a waveform, as is normally done in general audio coders

More information

LOSS CONCEALMENTS FOR LOW-BIT-RATE PACKET VOICE IN VOIP. Outline

LOSS CONCEALMENTS FOR LOW-BIT-RATE PACKET VOICE IN VOIP. Outline LOSS CONCEALMENTS FOR LOW-BIT-RATE PACKET VOICE IN VOIP Benjamin W. Wah Department of Electrical and Computer Engineering and the Coordinated Science Laboratory University of Illinois at Urbana-Champaign

More information

L19: Prosodic modification of speech

L19: Prosodic modification of speech L19: Prosodic modification of speech Time-domain pitch synchronous overlap add (TD-PSOLA) Linear-prediction PSOLA Frequency-domain PSOLA Sinusoidal models Harmonic + noise models STRAIGHT This lecture

More information

Audio Signal Compression using DCT and LPC Techniques

Audio Signal Compression using DCT and LPC Techniques Audio Signal Compression using DCT and LPC Techniques P. Sandhya Rani#1, D.Nanaji#2, V.Ramesh#3,K.V.S. Kiran#4 #Student, Department of ECE, Lendi Institute Of Engineering And Technology, Vizianagaram,

More information

Analog and Telecommunication Electronics

Analog and Telecommunication Electronics Politecnico di Torino - ICT School Analog and Telecommunication Electronics D5 - Special A/D converters» Differential converters» Oversampling, noise shaping» Logarithmic conversion» Approximation, A and

More information

Mel Spectrum Analysis of Speech Recognition using Single Microphone

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

More information

Waveform Coding Algorithms: An Overview

Waveform Coding Algorithms: An Overview August 24, 2012 Waveform Coding Algorithms: An Overview RWTH Aachen University Compression Algorithms Seminar Report Summer Semester 2012 Adel Zaalouk - 300374 Aachen, Germany Contents 1 An Introduction

More information

Signal Processing for Speech Applications - Part 2-1. Signal Processing For Speech Applications - Part 2

Signal Processing for Speech Applications - Part 2-1. Signal Processing For Speech Applications - Part 2 Signal Processing for Speech Applications - Part 2-1 Signal Processing For Speech Applications - Part 2 May 14, 2013 Signal Processing for Speech Applications - Part 2-2 References Huang et al., Chapter

More information

Speech/Non-speech detection Rule-based method using log energy and zero crossing rate

Speech/Non-speech detection Rule-based method using log energy and zero crossing rate Digital Speech Processing- Lecture 14A Algorithms for Speech Processing Speech Processing Algorithms Speech/Non-speech detection Rule-based method using log energy and zero crossing rate Single speech

More information

Reading: Johnson Ch , Ch.5.5 (today); Liljencrants & Lindblom; Stevens (Tues) reminder: no class on Thursday.

Reading: Johnson Ch , Ch.5.5 (today); Liljencrants & Lindblom; Stevens (Tues) reminder: no class on Thursday. L105/205 Phonetics Scarborough Handout 7 10/18/05 Reading: Johnson Ch.2.3.3-2.3.6, Ch.5.5 (today); Liljencrants & Lindblom; Stevens (Tues) reminder: no class on Thursday Spectral Analysis 1. There are

More information

Implementation of attractive Speech Quality for Mixed Excited Linear Prediction

Implementation of attractive Speech Quality for Mixed Excited Linear Prediction IOSR Journal of Electrical and Electronics Engineering (IOSR-JEEE) e-issn: 2278-1676,p-ISSN: 2320-3331, Volume 9, Issue 2 Ver. I (Mar Apr. 2014), PP 07-12 Implementation of attractive Speech Quality for

More information

ENHANCED TIME DOMAIN PACKET LOSS CONCEALMENT IN SWITCHED SPEECH/AUDIO CODEC.

ENHANCED TIME DOMAIN PACKET LOSS CONCEALMENT IN SWITCHED SPEECH/AUDIO CODEC. ENHANCED TIME DOMAIN PACKET LOSS CONCEALMENT IN SWITCHED SPEECH/AUDIO CODEC Jérémie Lecomte, Adrian Tomasek, Goran Marković, Michael Schnabel, Kimitaka Tsutsumi, Kei Kikuiri Fraunhofer IIS, Erlangen, Germany,

More information

Speech Signal Analysis

Speech Signal Analysis Speech Signal Analysis Hiroshi Shimodaira and Steve Renals Automatic Speech Recognition ASR Lectures 2&3 14,18 January 216 ASR Lectures 2&3 Speech Signal Analysis 1 Overview Speech Signal Analysis for

More information

Waveform interpolation speech coding

Waveform interpolation speech coding University of Wollongong Research Online University of Wollongong Thesis Collection 1954-2016 University of Wollongong Thesis Collections 1998 Waveform interpolation speech coding Jun Ni University of

More information

Voice Transmission --Basic Concepts--

Voice Transmission --Basic Concepts-- Voice Transmission --Basic Concepts-- Voice---is analog in character and moves in the form of waves. 3-important wave-characteristics: Amplitude Frequency Phase Telephone Handset (has 2-parts) 2 1. Transmitter

More information

A Closed-loop Multimode Variable Bit Rate Characteristic Waveform Interpolation Coder

A Closed-loop Multimode Variable Bit Rate Characteristic Waveform Interpolation Coder A Closed-loop Multimode Variable Bit Rate Characteristic Waveform Interpolation Coder Jing Wang, Jingg Kuang, and Shenghui Zhao Research Center of Digital Communication Technology,Department of Electronic

More information

QUESTION BANK EC 1351 DIGITAL COMMUNICATION YEAR / SEM : III / VI UNIT I- PULSE MODULATION PART-A (2 Marks) 1. What is the purpose of sample and hold

QUESTION BANK EC 1351 DIGITAL COMMUNICATION YEAR / SEM : III / VI UNIT I- PULSE MODULATION PART-A (2 Marks) 1. What is the purpose of sample and hold QUESTION BANK EC 1351 DIGITAL COMMUNICATION YEAR / SEM : III / VI UNIT I- PULSE MODULATION PART-A (2 Marks) 1. What is the purpose of sample and hold circuit 2. What is the difference between natural sampling

More information

Voice Codec for Floating Point Processor. Hans Engström & Johan Ross

Voice Codec for Floating Point Processor. Hans Engström & Johan Ross Voice Codec for Floating Point Processor Hans Engström & Johan Ross LiTH-ISY-EX--08/3782--SE Linköping 2008 Voice Codec for Floating Point Processor Master Thesis In Electronics Design, Dept. Of Electrical

More information

Introduction to Speech Coding. Nimrod Peleg Update: Oct. 2009

Introduction to Speech Coding. Nimrod Peleg Update: Oct. 2009 Introduction to Speech Coding Nimrod Peleg Update: Oct. 2009 Goals and Tradeoffs Reduce bitrate while preserving needed quality Tradeoffs: Quality (Broadcast, Toll, Communication, Synthetic) Bit Rate Complexity

More information

On a Classification of Voiced/Unvoiced by using SNR for Speech Recognition

On a Classification of Voiced/Unvoiced by using SNR for Speech Recognition International Conference on Advanced Computer Science and Electronics Information (ICACSEI 03) On a Classification of Voiced/Unvoiced by using SNR for Speech Recognition Jongkuk Kim, Hernsoo Hahn Department

More information

SPEECH ANALYSIS-SYNTHESIS FOR SPEAKER CHARACTERISTIC MODIFICATION

SPEECH ANALYSIS-SYNTHESIS FOR SPEAKER CHARACTERISTIC MODIFICATION M.Tech. Credit Seminar Report, Electronic Systems Group, EE Dept, IIT Bombay, submitted November 04 SPEECH ANALYSIS-SYNTHESIS FOR SPEAKER CHARACTERISTIC MODIFICATION G. Gidda Reddy (Roll no. 04307046)

More information

Fundamentals of Digital Communication

Fundamentals of Digital Communication Fundamentals of Digital Communication Network Infrastructures A.A. 2017/18 Digital communication system Analog Digital Input Signal Analog/ Digital Low Pass Filter Sampler Quantizer Source Encoder Channel

More information

EE482: Digital Signal Processing Applications

EE482: Digital Signal Processing Applications Professor Brendan Morris, SEB 3216, brendan.morris@unlv.edu EE482: Digital Signal Processing Applications Spring 2014 TTh 14:30-15:45 CBC C222 Lecture 14 Quiz 04 Review 14/04/07 http://www.ee.unlv.edu/~b1morris/ee482/

More information

REAL-TIME IMPLEMENTATION OF A VARIABLE RATE CELP SPEECH CODEC

REAL-TIME IMPLEMENTATION OF A VARIABLE RATE CELP SPEECH CODEC REAL-TIME IMPLEMENTATION OF A VARIABLE RATE CELP SPEECH CODEC Robert Zopf B.A.Sc. Simon Fraser University, 1993 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF

More information

International Journal of Advanced Engineering Technology E-ISSN

International Journal of Advanced Engineering Technology E-ISSN Research Article ARCHITECTURAL STUDY, IMPLEMENTATION AND OBJECTIVE EVALUATION OF CODE EXCITED LINEAR PREDICTION BASED GSM AMR 06.90 SPEECH CODER USING MATLAB Bhatt Ninad S. 1 *, Kosta Yogesh P. 2 Address

More information

Ninad Bhatt Yogeshwar Kosta

Ninad Bhatt Yogeshwar Kosta DOI 10.1007/s10772-012-9178-9 Implementation of variable bitrate data hiding techniques on standard and proposed GSM 06.10 full rate coder and its overall comparative evaluation of performance Ninad Bhatt

More information

CODING TECHNIQUES FOR ANALOG SOURCES

CODING TECHNIQUES FOR ANALOG SOURCES CODING TECHNIQUES FOR ANALOG SOURCES Prof.Pratik Tawde Lecturer, Electronics and Telecommunication Department, Vidyalankar Polytechnic, Wadala (India) ABSTRACT Image Compression is a process of removing

More information

Distributed Speech Recognition Standardization Activity

Distributed Speech Recognition Standardization Activity Distributed Speech Recognition Standardization Activity Alex Sorin, Ron Hoory, Dan Chazan Telecom and Media Systems Group June 30, 2003 IBM Research Lab in Haifa Advanced Speech Enabled Services ASR App

More information

Final draft ETSI EN V1.3.0 ( )

Final draft ETSI EN V1.3.0 ( ) European Standard (Telecommunications series) Terrestrial Trunked Radio (TETRA); Speech codec for full-rate traffic channel; Part 2: TETRA codec 2 Reference REN/TETRA-05059 Keywords TETRA, radio, codec

More information

Pulse Code Modulation (PCM)

Pulse Code Modulation (PCM) Pulse Code Modulation (PCM) PCM in the Bell System Multiplexing PCM Asynchronous PCM Extensions to PCM Differential PCM (DPCM) Adaptive DPCM (ADPCM) Delta-Sigma Modulation (DM) Vocoders PCM in the Bell

More information

Multi-Band Excitation Vocoder

Multi-Band Excitation Vocoder Multi-Band Excitation Vocoder RLE Technical Report No. 524 March 1987 Daniel W. Griffin Research Laboratory of Electronics Massachusetts Institute of Technology Cambridge, MA 02139 USA This work has been

More information

Introduction to Digital Communications System

Introduction to Digital Communications System Wireless Information Transmission System Lab. Introduction to Digital Communications System Institute of Communications Engineering National Sun Yat-sen University Recommended Books Digital Communications

More information

-voiced. +voiced. /z/ /s/ Last Lecture. Digital Speech Processing. Overview of Speech Processing. Example on Sound Source Feature

-voiced. +voiced. /z/ /s/ Last Lecture. Digital Speech Processing. Overview of Speech Processing. Example on Sound Source Feature ENEE408G Lecture-6 Digital Speech rocessing URL: http://www.ece.umd.edu/class/enee408g/ Slides included here are based on Spring 005 offering in the order of introduction, image, video, speech, and audio.

More information

Effects of Reverberation on Pitch, Onset/Offset, and Binaural Cues

Effects of Reverberation on Pitch, Onset/Offset, and Binaural Cues Effects of Reverberation on Pitch, Onset/Offset, and Binaural Cues DeLiang Wang Perception & Neurodynamics Lab The Ohio State University Outline of presentation Introduction Human performance Reverberation

More information

DECOMPOSITION OF SPEECH INTO VOICED AND UNVOICED COMPONENTS BASED ON A KALMAN FILTERBANK

DECOMPOSITION OF SPEECH INTO VOICED AND UNVOICED COMPONENTS BASED ON A KALMAN FILTERBANK DECOMPOSITIO OF SPEECH ITO VOICED AD UVOICED COMPOETS BASED O A KALMA FILTERBAK Mark Thomson, Simon Boland, Michael Smithers 3, Mike Wu & Julien Epps Motorola Labs, Botany, SW 09 Cross Avaya R & D, orth

More information

Signal Processing Toolbox

Signal Processing Toolbox Signal Processing Toolbox Perform signal processing, analysis, and algorithm development Signal Processing Toolbox provides industry-standard algorithms for analog and digital signal processing (DSP).

More information

Impact of the GSM AMR Speech Codec on Formant Information Important to Forensic Speaker Identification

Impact of the GSM AMR Speech Codec on Formant Information Important to Forensic Speaker Identification PAGE 483 Impact of the GSM AMR Speech Codec on Formant Information Important to Forensic Speaker Identification Bernard J Guillemin, Catherine I Watson Department of Electrical & Computer Engineering The

More information

T a large number of applications, and as a result has

T a large number of applications, and as a result has IEEE TRANSACTIONS ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING, VOL. 36, NO. 8, AUGUST 1988 1223 Multiband Excitation Vocoder DANIEL W. GRIFFIN AND JAE S. LIM, FELLOW, IEEE AbstractIn this paper, we present

More information

NOISE SHAPING IN AN ITU-T G.711-INTEROPERABLE EMBEDDED CODEC

NOISE SHAPING IN AN ITU-T G.711-INTEROPERABLE EMBEDDED CODEC NOISE SHAPING IN AN ITU-T G.711-INTEROPERABLE EMBEDDED CODEC Jimmy Lapierre 1, Roch Lefebvre 1, Bruno Bessette 1, Vladimir Malenovsky 1, Redwan Salami 2 1 Université de Sherbrooke, Sherbrooke (Québec),

More information

SGN Audio and Speech Processing

SGN Audio and Speech Processing Introduction 1 Course goals Introduction 2 SGN 14006 Audio and Speech Processing Lectures, Fall 2014 Anssi Klapuri Tampere University of Technology! Learn basics of audio signal processing Basic operations

More information

Chapter 4. Digital Audio Representation CS 3570

Chapter 4. Digital Audio Representation CS 3570 Chapter 4. Digital Audio Representation CS 3570 1 Objectives Be able to apply the Nyquist theorem to understand digital audio aliasing. Understand how dithering and noise shaping are done. Understand the

More information

3GPP TS V5.0.0 ( )

3GPP TS V5.0.0 ( ) TS 26.171 V5.0.0 (2001-03) Technical Specification 3rd Generation Partnership Project; Technical Specification Group Services and System Aspects; Speech Codec speech processing functions; AMR Wideband

More information

Lecture Outline. Data and Signals. Analogue Data on Analogue Signals. OSI Protocol Model

Lecture Outline. Data and Signals. Analogue Data on Analogue Signals. OSI Protocol Model Lecture Outline Data and Signals COMP312 Richard Nelson richardn@cs.waikato.ac.nz http://www.cs.waikato.ac.nz Analogue Data on Analogue Signals Digital Data on Analogue Signals Analogue Data on Digital

More information

TRANSFORMS / WAVELETS

TRANSFORMS / WAVELETS RANSFORMS / WAVELES ransform Analysis Signal processing using a transform analysis for calculations is a technique used to simplify or accelerate problem solution. For example, instead of dividing two

More information

Problems from the 3 rd edition

Problems from the 3 rd edition (2.1-1) Find the energies of the signals: a) sin t, 0 t π b) sin t, 0 t π c) 2 sin t, 0 t π d) sin (t-2π), 2π t 4π Problems from the 3 rd edition Comment on the effect on energy of sign change, time shifting

More information

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

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

More information

SGN Audio and Speech Processing

SGN Audio and Speech Processing SGN 14006 Audio and Speech Processing Introduction 1 Course goals Introduction 2! Learn basics of audio signal processing Basic operations and their underlying ideas and principles Give basic skills although

More information

The Opus Codec To be presented at the 135th AES Convention 2013 October New York, USA

The Opus Codec To be presented at the 135th AES Convention 2013 October New York, USA .ooo. The Opus Codec To be presented at the 135th AES Convention 2013 October 17 20 New York, USA This paper was accepted for publication at the 135 th AES Convention. This version of the paper is from

More information

Multirate DSP, part 3: ADC oversampling

Multirate DSP, part 3: ADC oversampling Multirate DSP, part 3: ADC oversampling Li Tan - May 04, 2008 Order this book today at www.elsevierdirect.com or by calling 1-800-545-2522 and receive an additional 20% discount. Use promotion code 92562

More information

Open Access Improved Frame Error Concealment Algorithm Based on Transform- Domain Mobile Audio Codec

Open Access Improved Frame Error Concealment Algorithm Based on Transform- Domain Mobile Audio Codec Send Orders for Reprints to reprints@benthamscience.ae The Open Electrical & Electronic Engineering Journal, 2014, 8, 527-535 527 Open Access Improved Frame Error Concealment Algorithm Based on Transform-

More information

Mobile Communications TCS 455

Mobile Communications TCS 455 Mobile Communications TCS 455 Dr. Prapun Suksompong prapun@siit.tu.ac.th Lecture 21 1 Office Hours: BKD 3601-7 Tuesday 14:00-16:00 Thursday 9:30-11:30 Announcements Read Chapter 9: 9.1 9.5 HW5 is posted.

More information

Systems for Audio and Video Broadcasting (part 2 of 2)

Systems for Audio and Video Broadcasting (part 2 of 2) Systems for Audio and Video Broadcasting (part 2 of 2) Ing. Karel Ulovec, Ph.D. CTU in Prague, Faculty of Electrical Engineering xulovec@fel.cvut.cz Only for study purposes for students of the! 1/30 Systems

More information

Signal Characterization in terms of Sinusoidal and Non-Sinusoidal Components

Signal Characterization in terms of Sinusoidal and Non-Sinusoidal Components Signal Characterization in terms of Sinusoidal and Non-Sinusoidal Components Geoffroy Peeters, avier Rodet To cite this version: Geoffroy Peeters, avier Rodet. Signal Characterization in terms of Sinusoidal

More information

IMPROVED CODING OF TONAL COMPONENTS IN MPEG-4 AAC WITH SBR

IMPROVED CODING OF TONAL COMPONENTS IN MPEG-4 AAC WITH SBR IMPROVED CODING OF TONAL COMPONENTS IN MPEG-4 AAC WITH SBR Tomasz Żernici, Mare Domańsi, Poznań University of Technology, Chair of Multimedia Telecommunications and Microelectronics, Polana 3, 6-965, Poznań,

More information

Signal Characteristics

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

More information

SOME PHYSICAL LAYER ISSUES. Lecture Notes 2A

SOME PHYSICAL LAYER ISSUES. Lecture Notes 2A SOME PHYSICAL LAYER ISSUES Lecture Notes 2A Delays in networks Propagation time or propagation delay, t prop Time required for a signal or waveform to propagate (or move) from one point to another point.

More information

SNR Scalability, Multiple Descriptions, and Perceptual Distortion Measures

SNR Scalability, Multiple Descriptions, and Perceptual Distortion Measures SNR Scalability, Multiple Descriptions, Perceptual Distortion Measures Jerry D. Gibson Department of Electrical & Computer Engineering University of California, Santa Barbara gibson@mat.ucsb.edu Abstract

More information

Chapter 2: Digitization of Sound

Chapter 2: Digitization of Sound Chapter 2: Digitization of Sound Acoustics pressure waves are converted to electrical signals by use of a microphone. The output signal from the microphone is an analog signal, i.e., a continuous-valued

More information

Super-Wideband Fine Spectrum Quantization for Low-rate High-Quality MDCT Coding Mode of The 3GPP EVS Codec

Super-Wideband Fine Spectrum Quantization for Low-rate High-Quality MDCT Coding Mode of The 3GPP EVS Codec Super-Wideband Fine Spectrum Quantization for Low-rate High-Quality DCT Coding ode of The 3GPP EVS Codec Presented by Srikanth Nagisetty, Hiroyuki Ehara 15 th Dec 2015 Topics of this Presentation Background

More information

Audio Compression using the MLT and SPIHT

Audio Compression using the MLT and SPIHT Audio Compression using the MLT and SPIHT Mohammed Raad, Alfred Mertins and Ian Burnett School of Electrical, Computer and Telecommunications Engineering University Of Wollongong Northfields Ave Wollongong

More information

Sampling and Reconstruction of Analog Signals

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

More information

Golomb-Rice Coding Optimized via LPC for Frequency Domain Audio Coder

Golomb-Rice Coding Optimized via LPC for Frequency Domain Audio Coder Golomb-Rice Coding Optimized via LPC for Frequency Domain Audio Coder Ryosue Sugiura, Yutaa Kamamoto, Noboru Harada, Hiroazu Kameoa and Taehiro Moriya Graduate School of Information Science and Technology,

More information

COMP 546, Winter 2017 lecture 20 - sound 2

COMP 546, Winter 2017 lecture 20 - sound 2 Today we will examine two types of sounds that are of great interest: music and speech. We will see how a frequency domain analysis is fundamental to both. Musical sounds Let s begin by briefly considering

More information

Waveform Interpolation Speech Coder at 4 kb/s

Waveform Interpolation Speech Coder at 4 kb/s Waveform Interpolation Speech Coder at 4 kb/s Eddie L. T. Choy Department of Electrical and Computer Engineering McGill University Montréal, Canada August 1998 A thesis submitted to the Faculty of Graduate

More information

Low Bit Rate Speech Coding Using Differential Pulse Code Modulation

Low Bit Rate Speech Coding Using Differential Pulse Code Modulation Advances in Research 8(3): 1-6, 2016; Article no.air.30234 ISSN: 2348-0394, NLM ID: 101666096 SCIENCEDOMAIN international www.sciencedomain.org Low Bit Rate Speech Coding Using Differential Pulse Code

More information

3. 3. Noncoherent Binary Modulation Techniques

3. 3. Noncoherent Binary Modulation Techniques 3. 3. Noncoherent Binary Modulation Techniques A digital communication receiver with no provision make for carrier phase recovery is said to be noncoherent. A. Noncoherent Orthogonal Modulation Scheme.

More information

CHAPTER 7 ROLE OF ADAPTIVE MULTIRATE ON WCDMA CAPACITY ENHANCEMENT

CHAPTER 7 ROLE OF ADAPTIVE MULTIRATE ON WCDMA CAPACITY ENHANCEMENT CHAPTER 7 ROLE OF ADAPTIVE MULTIRATE ON WCDMA CAPACITY ENHANCEMENT 7.1 INTRODUCTION Originally developed to be used in GSM by the Europe Telecommunications Standards Institute (ETSI), the AMR speech codec

More information

Experiment 2 Effects of Filtering

Experiment 2 Effects of Filtering Experiment 2 Effects of Filtering INTRODUCTION This experiment demonstrates the relationship between the time and frequency domains. A basic rule of thumb is that the wider the bandwidth allowed for the

More information

Data Encoding g(p (part 2)

Data Encoding g(p (part 2) Data Encoding g(p (part 2) CSE 3213 Instructor: U.T. Nguyen 10/11/2007 12:44 PM 1 Analog Data, Digital Signals (5.3) 2 1 Analog Data, Digital Signals Digitization Conversion of analog data into digital

More information

Physical Layer: Outline

Physical Layer: Outline 18-345: Introduction to Telecommunication Networks Lectures 3: Physical Layer Peter Steenkiste Spring 2015 www.cs.cmu.edu/~prs/nets-ece Physical Layer: Outline Digital networking Modulation Characterization

More information

Multirate Digital Signal Processing

Multirate Digital Signal Processing Multirate Digital Signal Processing Basic Sampling Rate Alteration Devices Up-sampler - Used to increase the sampling rate by an integer factor Down-sampler - Used to increase the sampling rate by an integer

More information

8.5 Modulation of Signals

8.5 Modulation of Signals 8.5 Modulation of Signals basic idea and goals measuring atomic absorption without modulation measuring atomic absorption with modulation the tuned amplifier, diode rectifier and low pass the lock-in amplifier

More information

Speech Compression. Application Scenarios

Speech Compression. Application Scenarios Speech Compression Application Scenarios Multimedia application Live conversation? Real-time network? Video telephony/conference Yes Yes Business conference with data sharing Yes Yes Distance learning

More information

Perception of pitch. Importance of pitch: 2. mother hemp horse. scold. Definitions. Why is pitch important? AUDL4007: 11 Feb A. Faulkner.

Perception of pitch. Importance of pitch: 2. mother hemp horse. scold. Definitions. Why is pitch important? AUDL4007: 11 Feb A. Faulkner. Perception of pitch AUDL4007: 11 Feb 2010. A. Faulkner. See Moore, BCJ Introduction to the Psychology of Hearing, Chapter 5. Or Plack CJ The Sense of Hearing Lawrence Erlbaum, 2005 Chapter 7 1 Definitions

More information

17. Delta Modulation

17. Delta Modulation 7. Delta Modulation Introduction So far, we have seen that the pulse-code-modulation (PCM) technique converts analogue signals to digital format for transmission. For speech signals of 3.2kHz bandwidth,

More information

The quality of the transmission signal The characteristics of the transmission medium. Some type of transmission medium is required for transmission:

The quality of the transmission signal The characteristics of the transmission medium. Some type of transmission medium is required for transmission: Data Transmission The successful transmission of data depends upon two factors: The quality of the transmission signal The characteristics of the transmission medium Some type of transmission medium is

More information

ITU-T EV-VBR: A ROBUST 8-32 KBIT/S SCALABLE CODER FOR ERROR PRONE TELECOMMUNICATIONS CHANNELS

ITU-T EV-VBR: A ROBUST 8-32 KBIT/S SCALABLE CODER FOR ERROR PRONE TELECOMMUNICATIONS CHANNELS 6th European Signal Processing Conference (EUSIPCO 008), Lausanne, Switzerland, August 5-9, 008, copyright by EURASIP ITU-T EV-VBR: A ROBUST 8- KBIT/S SCALABLE CODER FOR ERROR PRONE TELECOMMUNICATIONS

More information

A NEW FEATURE VECTOR FOR HMM-BASED PACKET LOSS CONCEALMENT

A NEW FEATURE VECTOR FOR HMM-BASED PACKET LOSS CONCEALMENT A NEW FEATURE VECTOR FOR HMM-BASED PACKET LOSS CONCEALMENT L. Koenig (,2,3), R. André-Obrecht (), C. Mailhes (2) and S. Fabre (3) () University of Toulouse, IRIT/UPS, 8 Route de Narbonne, F-362 TOULOUSE

More information

Quantisation mechanisms in multi-protoype waveform coding

Quantisation mechanisms in multi-protoype waveform coding University of Wollongong Research Online University of Wollongong Thesis Collection 1954-2016 University of Wollongong Thesis Collections 1996 Quantisation mechanisms in multi-protoype waveform coding

More information

UNIT IV FIR FILTER DESIGN 1. How phase distortion and delay distortion are introduced? The phase distortion is introduced when the phase characteristics of a filter is nonlinear within the desired frequency

More information

Ch. 3: Image Compression Multimedia Systems

Ch. 3: Image Compression Multimedia Systems 4/24/213 Ch. 3: Image Compression Multimedia Systems Prof. Ben Lee (modified by Prof. Nguyen) Oregon State University School of Electrical Engineering and Computer Science Outline Introduction JPEG Standard

More information

EXPERIMENT WISE VIVA QUESTIONS

EXPERIMENT WISE VIVA QUESTIONS EXPERIMENT WISE VIVA QUESTIONS Pulse Code Modulation: 1. Draw the block diagram of basic digital communication system. How it is different from analog communication system. 2. What are the advantages of

More information

EIE 441 Advanced Digital communications

EIE 441 Advanced Digital communications EIE 441 Advanced Digital communications MACHED FILER 1. Consider the signal s ( ) shown in Fig. 1. 1 t (a) Determine the impulse response of a filter matched to this signal and sketch it as a function

More information

Introduction to Wavelet Transform. Chapter 7 Instructor: Hossein Pourghassem

Introduction to Wavelet Transform. Chapter 7 Instructor: Hossein Pourghassem Introduction to Wavelet Transform Chapter 7 Instructor: Hossein Pourghassem Introduction Most of the signals in practice, are TIME-DOMAIN signals in their raw format. It means that measured signal is a

More information

NOISE ESTIMATION IN A SINGLE CHANNEL

NOISE ESTIMATION IN A SINGLE CHANNEL SPEECH ENHANCEMENT FOR CROSS-TALK INTERFERENCE by Levent M. Arslan and John H.L. Hansen Robust Speech Processing Laboratory Department of Electrical Engineering Box 99 Duke University Durham, North Carolina

More information

CHAPTER 4. PULSE MODULATION Part 2

CHAPTER 4. PULSE MODULATION Part 2 CHAPTER 4 PULSE MODULATION Part 2 Pulse Modulation Analog pulse modulation: Sampling, i.e., information is transmitted only at discrete time instants. e.g. PAM, PPM and PDM Digital pulse modulation: Sampling

More information

QUESTION BANK (VI SEM ECE) (DIGITAL COMMUNICATION)

QUESTION BANK (VI SEM ECE) (DIGITAL COMMUNICATION) QUESTION BANK (VI SEM ECE) (DIGITAL COMMUNICATION) UNIT-I: PCM & Delta modulation system Q.1 Explain the difference between cross talk & intersymbol interference. Q.2 What is Quantization error? How does

More information

DISCRETE FOURIER TRANSFORM AND FILTER DESIGN

DISCRETE FOURIER TRANSFORM AND FILTER DESIGN DISCRETE FOURIER TRANSFORM AND FILTER DESIGN N. C. State University CSC557 Multimedia Computing and Networking Fall 2001 Lecture # 03 Spectrum of a Square Wave 2 Results of Some Filters 3 Notation 4 x[n]

More information

Chapter 4. Part 2(a) Digital Modulation Techniques

Chapter 4. Part 2(a) Digital Modulation Techniques Chapter 4 Part 2(a) Digital Modulation Techniques Overview Digital Modulation techniques Bandpass data transmission Amplitude Shift Keying (ASK) Phase Shift Keying (PSK) Frequency Shift Keying (FSK) Quadrature

More information

Evaluation of clipping-noise suppression of stationary-noisy speech based on spectral compensation

Evaluation of clipping-noise suppression of stationary-noisy speech based on spectral compensation Evaluation of clipping-noise suppression of stationary-noisy speech based on spectral compensation Takahiro FUKUMORI ; Makoto HAYAKAWA ; Masato NAKAYAMA 2 ; Takanobu NISHIURA 2 ; Yoichi YAMASHITA 2 Graduate

More information

Digital Modulation Schemes

Digital Modulation Schemes Digital Modulation Schemes 1. In binary data transmission DPSK is preferred to PSK because (a) a coherent carrier is not required to be generated at the receiver (b) for a given energy per bit, the probability

More information

DIGITAL COMMUNICATIONS SYSTEMS. MSc in Electronic Technologies and Communications

DIGITAL COMMUNICATIONS SYSTEMS. MSc in Electronic Technologies and Communications DIGITAL COMMUNICATIONS SYSTEMS MSc in Electronic Technologies and Communications Bandpass binary signalling The common techniques of bandpass binary signalling are: - On-off keying (OOK), also known as

More information

Aspiration Noise during Phonation: Synthesis, Analysis, and Pitch-Scale Modification. Daryush Mehta

Aspiration Noise during Phonation: Synthesis, Analysis, and Pitch-Scale Modification. Daryush Mehta Aspiration Noise during Phonation: Synthesis, Analysis, and Pitch-Scale Modification Daryush Mehta SHBT 03 Research Advisor: Thomas F. Quatieri Speech and Hearing Biosciences and Technology 1 Summary Studied

More information

ZLS38500 Firmware for Handsfree Car Kits

ZLS38500 Firmware for Handsfree Car Kits Firmware for Handsfree Car Kits Features Selectable Acoustic and Line Cancellers (AEC & LEC) Programmable echo tail cancellation length from 8 to 256 ms Reduction - up to 20 db for white noise and up to

More information

Speech and Music Discrimination based on Signal Modulation Spectrum.

Speech and Music Discrimination based on Signal Modulation Spectrum. Speech and Music Discrimination based on Signal Modulation Spectrum. Pavel Balabko June 24, 1999 1 Introduction. This work is devoted to the problem of automatic speech and music discrimination. As we

More information

Lab.3. Tutorial : (draft) Introduction to CODECs

Lab.3. Tutorial : (draft) Introduction to CODECs Lab.3. Tutorial : (draft) Introduction to CODECs Fig. Basic digital signal processing system Definition A codec is a device or computer program capable of encoding or decoding a digital data stream or

More information