PCM DM 1 PulseCode Modulation PCM n In

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PCM & DM 1

PCM & DM 1

Pulse-Code Modulation (PCM) : n In PCM each sample of the signal is quantized

Pulse-Code Modulation (PCM) : n In PCM each sample of the signal is quantized to one of the amplitude levels, where B is the number of bits used to represent each sample. âThe rate from the source is n bps. The quantized waveform is modeled as : n q(n) represent the quantization error, Which we treat as an additive noise. 2

Pulse-Code Modulation (PCM) : ¨ The quantization noise is characterized as a realization of

Pulse-Code Modulation (PCM) : ¨ The quantization noise is characterized as a realization of a stationary random process q in which each of the random variables q(n) has uniform pdf. n Where the step size of the quantizer is 3

Pulse-Code Modulation (PCM) : ¨ If : maximum amplitude of signal, ¨ The mean

Pulse-Code Modulation (PCM) : ¨ If : maximum amplitude of signal, ¨ The mean square value of the quantization error is : ¨ Measure in d. B, The mean square value of the noise is : 4

Pulse-Code Modulation (PCM) : ¨ The quantization noise decreases by 6 d. B/bit. ¨

Pulse-Code Modulation (PCM) : ¨ The quantization noise decreases by 6 d. B/bit. ¨ If the headroom factor is h, then The signal to noise (S/N) ratio is given by (Amax=1) n ¨ In d. B, this is 5

Pulse-Code Modulation (PCM) : n Example : ¨ We require an S/N ratio of

Pulse-Code Modulation (PCM) : n Example : ¨ We require an S/N ratio of 60 d. B and that a headroom factor of 4 is acceptable. Then the required word length is : ¨ 60=10. 8 + 6 B – 20 ¨ If we sample at 8 KHZ, then PCM require 6

Pulse-Code Modulation (PCM) : n A nonuniform quantizer characteristic is usually obtained by passing

Pulse-Code Modulation (PCM) : n A nonuniform quantizer characteristic is usually obtained by passing the signal through a nonlinear device that compress the signal amplitude, follow by a uniform quantizer. Compressor A/D D/A Expander Compander (Compressor-Expander) Compressor-Expande 7

Companding: Compression and Expanding Original Signal After Compressing, Before Expanding 8

Companding: Compression and Expanding Original Signal After Compressing, Before Expanding 8

Companding n A logarithmic compressor employed in North American telecommunications systems has input-output magnitude

Companding n A logarithmic compressor employed in North American telecommunications systems has input-output magnitude characteristic of the form n is a parameter that is selected to give the desired compression characteristic. 9

Companding 10

Companding 10

Companding n The logarithmic compressor used in European telecommunications system is called A-law and

Companding n The logarithmic compressor used in European telecommunications system is called A-law and is defined as 11

Companding 12

Companding 12

DPCM : n A Sampled sequence u(m), m=0 to m=n-1. n Let be the

DPCM : n A Sampled sequence u(m), m=0 to m=n-1. n Let be the value of the reproduced (decoded) sequence. 13

DPCM: n At m=n, when u(n) arrives, a quantify , an estimate of u(n),

DPCM: n At m=n, when u(n) arrives, a quantify , an estimate of u(n), is predicted from the previously decoded samples i. e. , ¨ n ”prediction rule” Prediction error: 14

DPCM : n If is the quantized value of e(n), then the reproduced value

DPCM : n If is the quantized value of e(n), then the reproduced value of u(n) is: n Note: 15

DPCM CODEC: Σ Communication Channel Quantizer Predictor Coder Σ Σ Predictor Decoder 16

DPCM CODEC: Σ Communication Channel Quantizer Predictor Coder Σ Σ Predictor Decoder 16

DPCM: n Remarks: ¨ The pointwise coding error in the input sequence is exactly

DPCM: n Remarks: ¨ The pointwise coding error in the input sequence is exactly equal to q(n), the quantization error in e(n). ¨ With a reasonable predictor the mean sequare value of the differential signal e(n) is much smaller than that of u(n). 17

DPCM: n Conclusion: ¨ For the same mean square quantization error, e(n) requires fewer

DPCM: n Conclusion: ¨ For the same mean square quantization error, e(n) requires fewer quantization bits than u(n). ¨ The number of bits required for transmission has been reduced while the quantization error is kept the same. 18

DPCM modified by the addition of linearly filtered error sequence Communication Quantizer Σ Linear

DPCM modified by the addition of linearly filtered error sequence Communication Quantizer Σ Linear filter Coder Σ Channel Linear filter Σ Decoder 19

Adaptive PCM and Adaptive DPCM n Speech signals are quasi-stationary in nature â The

Adaptive PCM and Adaptive DPCM n Speech signals are quasi-stationary in nature â The variance and the autocorrelation function of the source output vary slowly with time. n PCM and DPCM assume that the source output is stationary. n The efficiency and performance of these encoders can be improved by adaptation to the slowly time-variant statistics of the speech signal. n Adaptive quantizer ¨ feedforward ¨ feedbackward 20

Example of quantizer with an adaptive step size 111 M (4) 7∆/2 M (3)

Example of quantizer with an adaptive step size 111 M (4) 7∆/2 M (3) 101 M (2) 3∆/2 -3∆ -2∆ 001 M (3) 000 M (4) 100 M (1) -∆ 011 0 ∆ -∆/2 M (1) 010 M (2) Multiplier 110 5∆/2 Previous Output 2∆ 3∆ -3∆/2 -5∆/2 -7∆/2 21

ADPCM with adaptation of the predictor Step-size adaptation Σ Quantizer Encoder Communication Channel Decoder

ADPCM with adaptation of the predictor Step-size adaptation Σ Quantizer Encoder Communication Channel Decoder Σ Σ Predictor adaptation Coder Decoder 22

Delta Modulation : (DM) n Predictor : one-step delay function n Quantizer : 1

Delta Modulation : (DM) n Predictor : one-step delay function n Quantizer : 1 -bit quantizer 23

Delta Modulation : (DM) n Primary Limitation of DM ¨ Slope overload : large

Delta Modulation : (DM) n Primary Limitation of DM ¨ Slope overload : large jump region n Max. slope = (step size)X(sampling freq. ) ¨ Granularity ¨ Instability Noise : almost constant region to channel noise 24

DM: Unit Delay Integrator Coder Unit Delay Decoder 25

DM: Unit Delay Integrator Coder Unit Delay Decoder 25

DM: Step size effect : Step Size (i) slope overload (sampling frequency ) (ii)

DM: Step size effect : Step Size (i) slope overload (sampling frequency ) (ii) granular Noise 26

Adaptive DM: Adaptive Function Unit Delay q. This adaptive approach simultaneously minimizes the effects

Adaptive DM: Adaptive Function Unit Delay q. This adaptive approach simultaneously minimizes the effects of both slope overload and granular noise 27

Vector Quantization (VQ) 28

Vector Quantization (VQ) 28

Vector Quantization : n Quantization is the process of approximating continuous amplitude signals by

Vector Quantization : n Quantization is the process of approximating continuous amplitude signals by discrete symbols. Partitioning of two-dimensional Space into 16 cells. n 29

Vector Quantization : The LBG algorithm first computes a 1 vector codebook, then uses

Vector Quantization : The LBG algorithm first computes a 1 vector codebook, then uses a splitting algorithm on the codeword to obtain the initial 2 -vector codebook, and continue the splitting process until the desired M-vector codebook is obtained. n This algorithm is known as the LBG algorithm proposed by Linde, Buzo and Gray. n 30

Vector Quantization : n The LBG Algorithm : ¨ Step 1: Set M (number

Vector Quantization : n The LBG Algorithm : ¨ Step 1: Set M (number of partitions or cells)=1. Find the centroid of all the training data. ¨ Step 2: Split M into 2 M partitions by splitting each current codeword by finding two points that are far apart in each partition using a heuristic method, and use these two points as the new centroids for the new 2 M codebook. Now set M=2 M. ¨ Step 3: Now use a iterative algorithm to reach the best set of centroids for the new codebook. ¨ Step 4: if M equals the VQ codebook size require, STOP; otherwise go to Step 2. 31