The document discusses digital signal processing techniques for converting analog signals to digital signals. It describes two main techniques: pulse code modulation (PCM) and delta modulation. PCM involves sampling, quantizing, and encoding an analog signal into binary digits. It allows digital signals to be more robust to noise than analog signals. Delta modulation predicts and encodes differences between signal values. Both techniques are used to convert analog voice signals for digital transmission. Companding techniques like A-law and μ-law coding apply logarithmic quantization to better match the human auditory system.

1. BY : MS. SURABHI TANKKAR
ME ETRX

2. •A digital signal is superior to an analog signal
because it is more robust to noise and can easily
be recovered, corrected and amplified.
• For this reason, the tendency today is to
change an analog signal to digital data.
•Generally used two techniques are :
pulse code modulation and
delta modulation
.

3. PCM
 PCM consists of three steps to digitize an analog
signal:
1. Sampling
2. Quantization
3. Binary encoding
 Before we sample, we have to filter the signal to limit
the maximum frequency of the signal as it affects the
sampling rate.
 Filtering should ensure that we do not distort the
signal, ie remove high frequency components that
affect the signal shape.

4. PCM ENCODER

5. SAMPLING
 Analog signal is sampled every TS secs.
 Ts is referred to as the sampling interval.
 fs = 1/Ts is called the sampling rate or sampling
frequency.
 There are 3 sampling methods:
 Ideal - an impulse at each sampling instant
 Natural - a pulse of short width with varying amplitude
 Flattop - sample and hold, like natural but with single
amplitude value

6. 3 DIFFERENT SAMPLING METHODS

7. Quantization
 Quantization is the process of “rounding off” a sample
according to some rule.

8. Nonuniform Quantizing
 Voice analog signals are more likely to have
amplitude values near zero than at the extreme peak
values allowed.
 For signals with nonuniform amplitude distribution,
the granular quantizing noise will be a serious
problem if the step size is not reduced for amplitude
values near zero and increased for extremely large
values. This is called nonuniform quantizing since a
variable step size is used.

9. Encoding
 Encoding is the process of representing the sampled
values as a binary number in the range 0 to n.
 The value of n is chosen as a power of 2, depending on
the accuracy required.
 Increasing n reduces the step size between adjacent
Quantization levels and hence reduces the
Quantization noise.
 The down side of this is that the amount of digital
data required to represent the analog signal increases.

10. Quantization Error and SNQR
 When a signal is quantized, we introduce an error - the coded signal
is an approximation of the actual amplitude value.
 The difference between actual and coded value (midpoint) is referred
to as the quantization error.
 Signals with lower amplitude values will suffer more from
quantization error as the error range: /2, is fixed for all signal levels.
 Non linear quantization is used to alleviate this problem. Goal is to
keep SNQR fixed for all sample values.
 Two approaches:
 The quantization levels follow a logarithmic curve. Smaller ’s at
lower amplitudes and larger ’s at higher amplitudes.
 Companding: The sample values are compressed at the sender
into logarithmic zones, and then expanded at the receiver. The
zon

11. PCM DECODER

12. PCM DECODER
 To recover an analog signal from a digitized signal
we follow the following steps:
 We use a hold circuit that holds the amplitude value of a
pulse till the next pulse arrives.
 We pass this signal through a low pass filter with a cutoff
frequency that is equal to the highest frequency in the
pre-sampled signal.

13. PCM TRANSMISSION SYSTEM

14. Companding
In telecommunication, signalprocessing,
companding (occasionally called compansion) is a
method of mitigating the detrimental effects of a
channel with limited dynamic range.
The name is a portmanteau of compressing and
expanding

15. A LAW & µ- LAW

16. A LAW & µ- LAW

17. Speech Companding
 The human auditory system is believed to be a
logarithmic process in which high amplitude sounds
do not require the same resolution as low amplitude
sounds.
 The human ear is more sensitive to quantization noise
in small signals than large signals.
 A-law and µ-law coding apply a logarithmic
quantization function to adjust the data resolution in
proportion to the level of the input signal.

18. Differential Pulse Code Modulation
(DPCM)
 quantises the difference between the original and the
predicted signals, i.e. the difference between successive
values.
 Leads to reduction in the number of bits used per sample
over that used for PCM. Using DPCM can reduce the bit
rate of voice transmission down to 48 kbps.
 DPCM can be described as a predictive coding scheme.

19. Adaptive Differential Pulse Code Modulation
(ADPCM)
 ADPCM adapts the Quantization levels of the
difference signal that is generated during the DPCM
process.
 If the difference signal is low, ADPCM reduces the size
of the Quantization levels.
 If the difference signal is high, ADPCM increases the
size of the Quantization levels.
 The Quantization level is thus adapted to the size of
the input difference signal, generating a uniform
signal-to-noise ratio throughout the dynamic range of
the difference signal.

20. Time Division Multiplexing (TDM)
in PCM
Transmitter Receiver
Timing Timing
Ch1 Ch1
i/p Buffer LPF1 o/p
Ch1 Transmission Line Ch1
i/p Buffer LPF2 o/p
SW1 SW2
Ch1 Ch1
i/p Buffer LPF3 o/p

21. Applications of PCM-TDM systems
 TDM and Codecs
 Digital Transmission Hierarchies
 Plesiochronous Digital Hierarchy (PDH)

22. Limitations of PCM systems
 Choosing a discrete value near the analog signal for each
sample leads to quantization error.
 Between samples no measurement of the signal is made;
the sampling theorem guarantees non-ambiguous
representation and recovery of the signal only if it has no
energy at frequency fs/2 or higher (one half the sampling
frequency, known as the Nyquist frequency); higher
frequencies will generally not be correctly represented or
recovered.
 As samples are dependent on time, an accurate clock is
required for accurate reproduction. If either the encoding
or decoding clock is not stable, its frequency drift will
directly affect the output quality of the device.

23. THANK YOU