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[MUSIC PLAYING]
ALAN OPPENHEIM: Hi,
I'm Alan Oppenheim.
And I'd like to welcome you
to this self-study course
on digital signal processing.
The fact that you're
interested in taking the course
suggests that you're probably
aware of the important role
that digital techniques
have been playing in signal
processing, in general.
And in fact, the impact
of digital technology
has been rather dramatic.
And the indications
are that it will
be even more so in the future.
One of the primary
advantages to digital as
opposed to analog signal
processing techniques
is the tremendous flexibility
that digital techniques
and digital signal
processing offers.
And because of this flexibility,
digital signal processing
techniques have
found application
in a rather large or
wide variety of areas.
Speech processing, for
example, has represented
one of the major
areas of application
of digital signal processing
for, at least, the past decade.
Both analysis of speech
and synthesis of speech
rely very heavily
on the notions of,
for example, digital
filtering, other notions,
such as the fast Fourier
transform algorithm,
and a variety of the
other digital signal
processing techniques
and algorithms.
More generally, in
communication systems,
digital signal processing
is being used for coding,
for multiplexing
and, in fact, there
is a considerable amount of work
being done at present directed
toward, basically, replacing
all of the present filtering
in communications
and telephone systems
by digital filters
instead of analog filters.
And I think that it's
likely that, in the not
too distant future,
we'll see most
of the filtering in
communication systems being
done digitally.
Seismic data
processing represents
another very important
area in which
the flexibility of
digital signal processing
is very heavily exploited.
In fact, seismic and
speech processing
have probably been the
two major catalysts
for most of the
important developments
in digital signal processing.
In audio recording
and processing,
digital signal processing
provides an opportunity
for some very sophisticated
processing and enhancement.
And in fact, fairly recently,
Professor Thomas Stockham
at the University
of Utah has been
applying some sophisticated
digital signal
processing techniques to the
restoration of old Caruso
recordings.
The problem, in that case,
is that the recordings that
were originally made
in the days of Caruso
involved a recording horn,
as I've illustrated here.
The singer, of course, singing
into the recording horn.
And the output of the
recording horn being
stored on a recording disk.
The problem in that
particular application
is the fact that the frequency
response of the recording horn
is not flat.
And what this tended to do is
give the resulting recording
a, sort of, megaphone
type of distortion.
And one of the objectives
in enhancing or restoring
some of these old
Caruso recordings
is to compensate, in a
sense, for the frequency
distortion introduced
by the recording horn.
What Professor
Stockham has done,
basically, is to use digital
signal processing techniques
to, first of all,
estimate the frequency
response of the recording horn.
And second, to compensate
for that frequency response.
And all of the work
that he carried out
was done digitally, primarily,
as I indicated previously,
primarily to capitalize
on the flexibility
that digital signal
processing offers.
And some of the results that he
obtained are rather dramatic.
And let me just illustrate
in a very short passage what
some of this has sounded like.
What I borrowed from
Professor Stockham
is a recording of
the restoration
that he generated on
a digital computer.
And this particular recording
is a two track recording
with the original
segment recorded
on channel 1 and the process
segment recorded on channel 2.
And that will allow us to switch
back and forth between these.
The particular piece
that is illustrated here
is a section from the famous
aria "Vesti La Giubba"
as sung, of course,
by Enrico Caruso.
So let me just quickly
illustrate this
as an example of some of
the type of processing
that is currently being done
using digital signal processing
techniques.
Let me begin, first
of all, by playing
a little bit of the original.
And then, I'll switch to the
result of Professor Stockholm's
enhancement.
And then, switch back
and forth a few times
to present a comparison.
So we begin, first of
all, with the original.
[MUSIC ENRICO CARUSO, "VESTI LA
 GIUBBA"]
And then switch to the enhanced.
[MUSIC ENRICO CARUSO, "VESTI LA
 GIUBBA"]
Back to the original.
[MUSIC ENRICO CARUSO, "VESTI
LA GIUBBA"]
And then, once more,
to the enhanced.
[MUSIC ENRICO CARUSO, "VESTI LA
 GIUBBA"]
And I think what you
can observe with that
is a fairly dramatic increase in
the improvement in the quality
of the recording.
Primarily, the megaphone type
of quality in the original
has been, essentially,
eliminated.
Now to go even further
in illustrating
some of the flexibility of
digital signal processing.
One of the things
that we observe
with this particular recording
and this particular example
is that, although there
is some enhancement that's
been implemented, there still
is some background noise that
is present in, both, the
original and the enhanced
or restored.
And so one of the
things, obviously,
that we would like to do is
remove this background noise.
In fact, using some rather
sophisticated signal processing
techniques, Professor Stockham,
together with Neil J. Miller,
have not only removed
the background noise
but, with the same
processing, removed also
the orchestral accompaniment.
Now this is, first of
all, rather dramatic.
Second of all, somewhat useful
in the sense that in carrying
out a complete restoration
one can imagine then
redubbing a new orchestra on
top of the restored recording.
So let me just play a little
bit of this to, in fact,
show you that it really has
been possible to not only remove
the background noise
but also to remove
the orchestral accompaniment.
So first let me move forward
on the tape to the right place.
And what you'll hear now
in this case, on channel 1,
is the Caruso recording restored
as I indicated previously.
And on channel 2 is the
result of further processing,
the restored recording,
to eliminate,
both, the background noise
and also the orchestra.
So we'll begin
with the restored,
which includes the orchestra.
And then, the orchestra removed.
[MUSIC ENRICO CARUSO, "VESTI LA
GIUBBA"]
That's with the orchestra.
[MUSIC ENRICO CARUSO, "VESTI LA
 GIUBBA"]
And with the orchestra and
the background noise removed.
[MUSIC ENRICO CARUSO, "VESTI LA
 GIUBBA"]
And then, once more, back to
the orchestral [INAUDIBLE]..
[MUSIC ENRICO CARUSO,
"VESTI LA GIUBBA"]
Well I think that
you'll probably
have to admit
that, in fact, it's
a rather dramatic example of
some sophisticated digital
signal processing.
Another area in which
digital signal processing
has tremendous
potential is in the area
of digital image processing.
And in that case,
the basic notion
is to treat an image as a two
dimensional signal digitized,
of course.
And one is then
afforded the possibility
of applying digital signal
processing techniques
to the two dimensional signal.
For example, in a very simple
signal processing environment,
we might be interested in low
pass filtering a digital image.
For example, if the image
has considerable grain noise,
grain noise, in fact,
tends to be high frequency.
And low pass filtering then
will tend to reduce or eliminate
noise of that type.
Or we might be interested
in high pass filtering.
For example, if we wanted to
enhance edges in a picture,
the procedure would
be or one procedure
might be to apply a two
dimensional high pass filter.
More elaborately,
we could consider
some processing, which is
directed at general image
enhancement.
And one example that I'd like to
show you is some digital image
processing that was
carried out directed
at simultaneously reducing
the dynamic range of an image
and increasing the
contrast of the image.
Generally,
photographically, these
are conflicting requirements.
But with some
sophisticated processing,
it's possible to simultaneously
reduce the dynamic range
and increase the contrast.
To illustrate how this
works with an example,
the first slide that I'll show
you is an original image, which
is, of course, a digital image
displayed on a computer scope.
And one of the things that
we notice about that image
is that it has a rather
high dynamic range.
For example, the snow
outside the boiler room
is rather bright.
The inside of the
boiler room is dark.
And of course, the contrast
inside the boiler room
is relatively low
because of the fact
that the illumination inside the
boiler room is relatively low.
So one type of processing
that we could consider
is the simultaneous
enhancement of contrast,
and reduction of dynamic
range, and applying some two
dimensional signal processing.
The result is what I show
you on the next slide
where, here, we've
processed to bring out
the detail inside
the boiler room.
You can notice that the dynamic
range, in fact, is reduced.
The snow is darker than
it was in the original.
The boiler room is brighter
than it was in the original,
suggesting reduced
dynamic range.
But also, the contrast
is very clearly enhanced.
Just as another example of
the same type of processing.
First, let's look
at an original where
we observe that there's a
brightly illuminated area,
which is where the
radome moves is.
And then, a more dimly
illuminated area.
The details in the
right hand corner
with the trees and leaves.
And as a result
of processing to,
again, increase the contrast
and reduce the dynamic range,
we see in the resulting
processed image
that the detail in the
dimly illuminated areas,
in fact, is brought out
rather dramatically.
So this is one example of
some rather sophisticated
digital signal
processing applied
to two dimensional signals.
Namely, to images.
And I should
mention incidentally
that the type of processing
that was applied for this image
enhancement is discussed in
considerable detail in chapter
10 of the text.
Now there are, of course, a
long list of other applications
of digital signal processing.
In the biomedical
area, for example,
digital signal
processing techniques
are playing a very
important role.
In radar and sonar, those
are two additional areas
in which digital
signal processing
is extremely important.
And I'm sure that
there are other areas
that you're aware of that
I, perhaps, might not
be where digital signal
processing is particularly
important because
of its flexibility.
Specifically, in
this course, we won't
be focusing on applications.
Although, it's important
to keep in mind as we
go through the course
that the material
that we're talking about has
very important applications.
In the course, we'll be
concentrating specifically
on the fundamentals and
techniques of digital signal
processing.
As I've indicated
in the study guide,
I will be assuming
that you previously
have had a course in linear
system theory, continuous time
linear system theory, including
Fourier and Laplace transforms.
But I will not be assuming
any specific background
in discrete time signals
and systems in Z-transforms,
et cetera.
In fact, in the next
lecture, lecture two,
we will begin from
the beginning.
Namely, we will begin with a
definition of discrete time
signals and systems.
And if you feel a little
rusty on basic linear system
theory for continuous
time systems,
it might be well
before then to do
just a little bit of reviewing.
And I suggest some possible
books in the study guide.
I would also suggest, before
beginning the next lecture,
that you read the
introduction to the text.
And perhaps, also browse through
the text and the study guide
to get a general impression
of the scope of the course,
and some of the
objectives of the course.
As I indicated,
next time, we will
begin with the definition
of discrete time signals
and systems.
I sincerely hope that you
find this set of lessons
to be interesting
and worthwhile.
And I'll see you at
the next lecture.
Thank you.
[MUSIC PLAYING]
