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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were originally made in the days of Caruso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]