analog voice interfaces. Figure illustrates a call
from an analog telephone (Phone1), that is connected to a
router (R1), to an analog telephone (Phone2) that is connected
to another router (R2). The two routers connect to an IP
network. When router R2 receives the IP packets carrying
digitized voice, the router converts the packets back to analog
signals. The analog signals go to Phone2 and play through the
speaker of the telephone; the user at Phone2 hears the original
speech. The digital-to-analog process is the reverse of the
analog-to-digital conversion. DSPs on the voice interface cards
of the voice-enabled routers convert digital signals to analog
signals. Figure summarizes the steps: Step 1
Decompression: Any compressed voice samples are first
decompressed. This is an optional step in converting analog
signals. Step 2 Decoding: The DSPs in the voice
interface card decode the digital voice samples to the
amplitude value of the samples and then rebuild a PAM signal of
the original amplitude. Step 3 Reconstruction of the
analog signal: The DSP passes the PAM signal through a
properly designed filter that removes the discrete digital
steps from the output and produces the smooth analog signal
mirroring the original analog waveform from the digitally coded
counterpart.
Content 2.2 Digitizing and
Packetizing Voice 2.2.3 Sampling When a DSP
converts an analog signal to digital form, the DSP samples the
analog signal first. The sampling rate impacts the quality of
the digitized signal. If the sampling rate is too low, the DSP
processes too little information and the resulting quality is
degraded. The Nyquist theorem is the basis of analog-to-digital
conversion. In simple terms, the Nyquist theorem tells us that
reconstruction of a signal from the signal samples is possible
if the sampling frequency is greater than twice the signal
bandwidth. In practical terms, reconstruction is neither
perfect nor exact. Engineers select sampling rates to meet the
practical requirements of specific applications. This topic
describes how to select a practical sampling rate. Figure
illustrates two situations. In the first situation, the
sampling rate is too low and the reconstructed information is
imprecise. Practical reconstruction is impossible. In the
second situation, a higher sampling rate is used, and the
resulting PAM signals represent the original waveform; this
situation allows for practical reconstruction. The Nyquist
theorem predicts how a DSP works; when the DSP samples a signal
instantaneously at regular intervals and at a rate of at least
twice the highest channel frequency, then the samples contain
sufficient information to allow an accurate reconstruction of
the signal at the receiver. The example in Figure illustrates
how engineers arrived at a rate of 8000 samples per second for
telephony applications. Although the human ear can sense sounds
from 20 to 20000 Hz, and speech encompasses sounds from about
200 to 9000 Hz, the telephone channels we use operate at about
300 to 3400 Hz. This economical range carries enough fidelity
to allow callers to identify who the party at the other end of
the connection is and to sense the other party’s mood. To allow
capturing of higher-frequency sounds that the telephone channel
can deliver, the highest frequency for voice was set to 4000
Hz. Using the Nyquist theorem, the sampling rate results in
8000 samples per second; that is, one sample every 125 ms.
Taking samples above the Nyquist rate is called oversampling.
Content 2.2 Digitizing and Packetizing
Voice 2.2.4 Quantization Telephony
applications use a sampling rate of 8000 MHz to convert an
analog signal to a digital format. The DSP must round the value
of each sample to the nearest integer on a scale that varies
according to the resolution of the signal. The DSP then
converts the integers to binary numbers. Quantization is the
process of selecting those binary numbers to represent the
voltage level of each sample (the pulse amplitude modulation
[PAM] value). In a sense, DSPs use quantization to approximate
analog sounds to the nearest binary value that is available.
The DSP must select a whole number that is closest to the
signal level the DSP is reading at the instant the signal is
sampled. The PAM values are rounded up or down to the step that
is closest to the original analog signal. The difference
between the original analog signal and the quantization level
assigned is called quantization error or quantization
noise. This difference is the source of distortion in
digital transmission systems. Note
Noise and
distortion are different phenomena. Distortion is any change in
the signal that results in the output being different from the
original. Noise is additional information/signals added to the
original. Noise is a form of error that is not as directly
related to the input signal. In other words, noise is
uncorrelated with the input signal. Noise is also random in
relation to distortion because it comes from outside the input
signal. In terms of measurement, distortion often sounds
“meaningful” even though it is not, and as such, distortion is
difficult to separate from noise. For this reason, distortion
can be more distracting in an audio signal than noise. The term
noise is often used in place of distortion. Telephony
applications usually use 8-bit quantization. DSPs represent all
possible values of the analog waveform with 256 distinct
voltage values, each represented by an 8-bit binary number.
These approximations are not an exact duplication of the analog
waveform and contain quantization errors (noise). By
comparison, compact discs use 16-bit quantization that allows
for 65,536 distinct voltage levels. Although 8-bit quantization
is crude and introduces substantial quantization noise into
the signal, the result is still more than adequate to represent
human speech in telephony applications. Figure depicts
quantization. In this example, the x-axis of the chart is time
and the y-axis of the chart is the voltage value (PAM). The
example shows quantization noise distortion at all signals that
do not exactly match one of the steps. Another important term
is signal-to-noise ratio. Signal-to-noise ratio (SNR) is the
ratio of a given transmitted signal to the background noise of
the transmission medium. An unfortunate reality of quantization
is that SNR is larger at signal amplitudes because the signal
is using a smaller portion of the available dynamic range. This
means errors are proportionally large relative to the signal.
To avoid these SNR issues, engineers use a logarithmic scale to
provide better granularity for smaller signals, resulting in a
more uniform SNR for all signals.
Content 2.2
Digitizing and Packetizing Voice 2.2.5 Digital
Voice Encoding Digital voice samples are represented by 8
bits per sample. Each sample is encoded in the following
way: - One polarity bit: Indicates positive or
negative signals
- Three segment bits: Identify
the logarithmically sized segment number (0–7)
-
Four step bits: Identify the linear step within a
segment
Because telephony sampling takes 8000
samples per second, the bandwidth that is needed per call is 64
kbps. This bandwidth need is why traditional circuit-switched
telephony networks use time-division-multiplexed lines,
combining multiple channels of 64 kbps each (digital signal
level 0 [DS-0]) in a single physical interface.
Content
2.2 Digitizing and Packetizing Voice
2.2.6 Companding Companding refers to the
process of first compressing an analog signal at the source and
then expanding this signal back to its original size when it
reaches its destination. The term companding comes from
combining the two terms, compressing and expanding, into one
word. A compander compresses input analog signal samples into
logarithmic segments. The compander then quantizes and codes
each segment using uniform quantization. Bell Systems defined