Telecommunications
:
Telecommunications is about transferring information from one location
to another. This includes many forms of information: telephone conversations,
television signals, computer files, and other types of data. To transfer
the information, you need a channel between the two locations. This may
be a wire pair, radio signal, optical fiber, etc. Telecommunications companies
receive payment for transferring their customer's information, while they
must pay to establish and maintain the channel. The financial bottom line
is simple: the more information they can pass through a single channel,
the more money they make. DSP has revolutionized the telecommunications
industry in many areas: signaling tone generation and detection, frequency
band shifting, filtering to remove power line hum, etc. Three specific
examples from the telephone network will be discussed here: multiplexing,
compression, and echo control.
Multiplexing :
There are approximately one billion telephones in the world. At the
press of a few buttons, switching networks allow any one of these to be
connected to any other in only a few seconds. The immensity of this task
is mind boggling! Until the 1960s, a connection between two telephones
required passing the analog voice signals through mechanical switches and
amplifiers. One connection required one pair of wires. In comparison, DSP
converts audio signals into a stream of serial digital data. Since bits
can be easily intertwined and later separated, many telephone conversations
can be transmitted on a single channel. For example, a telephone standard
known as the T-carrier system can simultaneously transmit 24 voice signals.
Each voice signal is sampled 8000 times per second using an 8 bit companded
(logarithmic compressed) analog-to-digital conversion. This results in
each voice signal being represented as 64,000 bits/sec, and all 24 channels
being contained in 1.544 megabits/sec. This signal can be transmitted about
6000 feet using ordinary telephone lines of 22 gauge copper wire, a typical
interconnection distance. The financial advantage of digital transmission
is enormous. Wire and analog switches are expensive; digital logic gates
are cheap.
Compression
:
When a voice signal is digitized at 8000 samples/sec, most of the digital
information is redundant. That is, the information carried by any one sample
is largely duplicated by the neighboring samples. Dozens of DSP algorithms
have been developed to convert digitized voice signals into data streams
that require fewer bits/sec. These are called data compression algorithms.
Matching uncompression algorithms are used to restore the signal to its
original form. These algorithms vary in the amount of compression achieved
and the resulting sound quality. In general, reducing the data rate from
64 kilobits/sec to 32 kilobits/sec results in no loss of sound quality.
When compressed to a data rate of 8 kilobits/sec, the sound is noticeably
affected, but still usable for long distance telephone networks. The highest
achievable compression is about 2 kilobits/sec, resulting in sound that
is highly distorted, but usable for some applications such as military
and undersea communications.
Echo control
:
Echoes are a serious problem in long distance telephone connections.
When you speak into a telephone, a signal representing your voice travels
to the connecting receiver, where a portion of it returns as an echo. If
the connection is within a few hundred miles, the elapsed time for receiving
the echo is only a few milliseconds. The human ear is accustomed to hearing
echoes with these small time delays, and the connection sounds quite normal.
As the distance becomes larger, the echo becomes increasingly noticeable
and irritating. The delay can be several hundred milliseconds for intercontinental
communications, and is particularity objectionable. Digital Signal Processing
attacks this type of problem by measuring the returned signal and generating
an appropriate antisignal to cancel the offending echo. This same technique
allows speakerphone users to hear and speak at the same time without fighting
audio feedback (squealing). It can also be used to reduce environmental
noise by canceling it with digitally generated antinoise.
Audio Processing
:
The two principal human senses are vision and hearing. Correspondingly,
much of DSP is related to image and audio processing. People listen to
both music and speech. DSP has made revolutionary changes in both these
areas.
Music
:
The path leading from the musician's microphone to the audiophile's
speaker is remarkably long. Digital data representation is important to
prevent the degradation commonly associated with analog storage and manipulation.
This is very familiar to anyone who has compared the musical quality of
cassette tapes with compact disks. In a typical scenario, a musical piece
is recorded in a sound studio on multiple channels or tracks. In some cases,
this even involves recording individual instruments and singers separately.
This is done to give the sound engineer greater flexibility in creating
the final product. The complex process of combining the individual tracks
into a final product is called mix down. DSP can provide several important
functions during mix down, including: filtering, signal addition and subtraction,
signal editing, etc. One of the most interesting DSP applications in music
preparation is artificial reverberation. If the individual channels are
simply added together, the resulting piece sounds frail and diluted, much
as if the musicians were playing outdoors. This is because listeners are
greatly influenced by the echo or reverberation content of the music, which
is usually minimized in the sound studio. DSP allows artificial echoes
and reverberation to be added during mix down to simulate various ideal
listening environments. Echoes with delays of a few hundred milliseconds
give the impression of cathedral like locations. Adding echoes with delays
of 10-20 milliseconds provide the perception of more modest size listening
rooms.
Speech generation
:
Speech generation and recognition are used to communicate between humans
and machines. Rather than using your hands and eyes, you use your mouth
and ears. This is very convenient when your hands and eyes should be doing
something else. Two approaches are used for computer generated speech:
digital recording and vocal tract simulation. In digital recording, the
voice of a human speaker is digitized and stored, usually in a compressed
form. During playback, the stored data are uncompressed and converted back
into an analog signal. An entire hour of recorded speech requires only
about three megabytes of storage, well within the capabilities of even
small computer systems. This is the most common method of digital speech
generation used today. Vocal tract simulators are more complicated, trying
to mimic the physical mechanisms by which humans create speech. The human
vocal tract is an acoustic cavity with resonate frequencies determined
by the size and shape of the chambers. Sound originates in the vocal tract
in one of two basic ways, called voiced and fricative sounds. With voiced
sounds, vocal cord vibration produces near periodic pulses of air into
the vocal cavities. In comparison, fricative sounds originate from the
noisy air turbulence at narrow constrictions, such as the teeth and lips.
Vocal tract simulators operate by generating digital signals that resemble
these two types of excitation. The characteristics of the resonate chamber
are simulated by passing the excitation signal through a digital filter
with similar resonance. This approach was used in one of the very early
DSP success stories, the Speak & Spell, a widely sold electronic learning
aid for children.
Speech recognition :
The automated recognition of human speech is immensely more difficult
than speech generation. Speech recognition is a classic example of things
that the human brain does well, but digital computers do poorly. Unfortunately,
present day computers perform very poorly when faced with raw sensory data.
Teaching a computer to send you a monthly electric bill is easy. Teaching
the same computer to understand your voice is a major undertaking. Digital
Signal Processing generally approaches the problem of voice recognition
in two steps: feature extraction followed by feature matching. Each word
in the incoming audio signal is isolated and then analyzed to identify
the type of excitation and resonate frequencies. These parameters are then
compared with previous examples of spoken words to identify the closest
match. Often, these systems are limited to only a few hundred words; can
only accept speech with distinct pauses between words; and must be retrained
for each individual speaker. While this is adequate for many commercial
applications, these limitations are humbling when compared to the abilities
of human hearing. There is a great deal of work to be done in this area.
Radar :
Radar is an acronym for Radio Detection And Ranging. In the simplest
radar system, a radio transmitter produces a pulse of radio frequency energy
a few microseconds long. This pulse is fed into a highly directional antenna,
where the resulting radio wave propagates away at the speed of light. Aircraft
in the path of this wave will reflect a small portion of the energy back
toward a receiving antenna, situated near the transmission site. The distance
to the object is calculated from the elapsed time between the transmitted
pulse and the received echo. The direction to the object is found more
simply; you know where you pointed the directional antenna when the echo
was received. The operating range of a radar system is determined by two
parameters: how much energy is in the initial pulse, and the noise level
of the radio receiver. Unfortunately, increasing the energy in the pulse
usually requires making the pulse longer. In turn, the longer pulse reduces
the accuracy and precision of the elapsed time measurement. This results
in a conflict between two important parameters: the ability to detect objects
at long range, and the ability to accurately determine an object's distance.
DSP has revolutionized radar in three areas, all of which relate to this
basic problem. First, DSP can compress the pulse after it is received,
providing better distance determination without reducing the operating
range. Second, DSP can filter the received signal to decrease the noise.
This increases the range, without degrading the distance determination.
Third, DSP enables the rapid selection and generation of different pulse
shapes and lengths. Among other things, this allows the pulse to be optimized
for a particular detection problem. Now the impressive part: much of this
is done at a sampling rate comparable to the radio frequency used, at high
as several hundred megahertz! When it comes to radar, DSP is as much about
high-speed hardware design as it is about algorithms.
Image Processing
:
Images are signals with special characteristics. First, they are a
measure of a parameter over space (distance), while most signals are a
measure of a parameter over time. Second, they contain a great deal of
information. For example, more than 10 megabytes can be required to store
one second of television video. This is more than a thousand times greater
than for a similar length voice signal. Third, the final judge of quality
is often a subjective human evaluation, rather than an objective criteria.
These special characteristics have made image processing a distinct subgroup
within DSP.
Medical :
In 1895, Wilhelm Conrad Röntgen discovered that x-rays could pass
through substantial amounts of matter. Medical x-ray systems spread throughout
the world in only a few years. In spite of its obvious success, medical
x-ray imaging was limited by four problems until DSP and related techniques
came along in the 1970s. First, overlapping structures in the body can
hide behind each other. For example, portions of the heart might not be
visible behind the ribs. Second, it is not always possible to distinguish
between similar tissues. For example, it may be able to separate bone from
soft tissue, but not distinguish a tumor from the liver. Third, x-ray images
show anatomy, the body's structure, and not physiology, the body's operation.
The x-ray image of a living person looks exactly like the x-ray image of
a dead one! Fourth, x-ray exposure can cause cancer, requiring it to be
used sparingly and only with proper justification. The problem of overlapping
structures was solved in 1971 with the introduction of the first computed
tomography scanner (formerly called computed axial tomography, or CAT scanner).
Computed tomography (CT) is a classic example of Digital Signal Processing.
X-rays from many directions are passed through the section of the patient's
body being examined. Instead of simply forming images with the detected
x-rays, the signals are converted into digital data and stored in a computer.
The information is then used to calculate images that appear to be slices
through the body. These images show much greater detail than conventional
techniques, allowing significantly better diagnosis and treatment. The
impact of CT was nearly as large as the original introduction of x-ray
imaging itself. Within only a few years, every major hospital in the world
had access to a CT scanner. In 1979, two of CT's principle contributors,
Godfrey N. Hounsfield and Allan M. Cormack, shared the Nobel Prize in Medicine.
That's good DSP! It plays a key role in all these techniques. For example,
Magnetic Resonance Imaging (MRI) uses magnetic fields in conjunction with
radio waves to probe the interior of the human body. Properly adjusting
the strength and frequency of the fields cause the atomic nuclei in a localized
region of the body to resonate between quantum energy states. This resonance
results in the emission of a secondary radio The Scientist and Engineer's
Guide to Digital Signal Processing 10 wave, detected with an antenna placed
near the body. The strength and other characteristics of this detected
signal provide information about the localized region in resonance. Adjustment
of the magnetic field allows the resonance region to be scanned throughout
the body, mapping the internal structure. This information is usually presented
as images, just as in computed tomography. Besides providing excellent
discrimination between different types of soft tissue, MRI can provide
information about physiology, such as blood flow through arteries. MRI
relies totally on Digital Signal Processing techniques, and could not be
implemented without them.