we got than
uh my name is
that was that in a little to and the to get that with the professor of S
from a you know that's
okay
and our team of
what what are you can
would like to present to a variable step-size the proposed and make a a type of in which we call
that
yeah or a solution for echo cancellation
uh
is the outline
as annotation
we can see
a what the production then uh
as the basis
of the proposed to eight of find projection
followed you followed by a
our proposed a and we and we
simulation up
company
now a sense to the nice presentation
yeah i
a scale but mission if we
if using production
basically we we deal with the same echo cancellation problem
we in fact is a
uh
system identification by with some challenge
this challenging in uh i have a union our maybe
is that a a long line
or the equal parts so a lot no think what they are not
if if you that and
maybe the double talk situations
see
is much
a challenging in there
a right
a this specific problem we
the the more give a task for some special Y
for example
if we want to
uh a faster
again and then choose the family of affine projection
oh
which we know that the
ah the
more efficient from this point of us compare
and that a man
and we also if we we won
to increase the robustness to double talk
we can use the body of a step size
we will see in the presentation
of course in general the as
a echo canceller
and keep with a little of detector but
yeah very useful
you is yeah that we've algorithm it somehow
oh was
but seven to that
and uh we have some of the hints let's say
uh
because course is the system we need to identify which are be
a a i i i
make is that the my is it
so it would be useful to the use
but to admit
i
adaptive of body
i i don't by means this three each with result
uh a the present a
now just the
oh
but if for the each of a you of the problem
uh this is the general
okay
separation
patient
what additional so
and the a lot of course
can be expressed in of a a ways for example from that use the point of view was the main
but
is to
but couple but C know the near a signal in the all yeah that
of course
for all a point of from the application point of view we have to cancel this K
well you not know what we have to like that if i is equal uh so this is the basic
and a the
application one
now i is a
uh talk uh and that the is the affine projection of what a a a a at may from the
convergence point of view
uh uh they but for very where especially we
the in it
signal inputs like speech
uh yeah creation
or the class
a find projection of are
you but yeah
uh
this is uh
it true signal vector but this as the input
signal signal my
and
most important this part i mean that you know the projection all that if you can see when P E
close to one
this is we used to a a lot and elements
this is a a a a if you and and
this is the step size for it that which we uh
the following
are those of its
H
now is that problem to me
a fine projection of what you can you thing by extending the idea of pnlms
in the same manner
as
we be form and lms the pnlms
we can do for
a yeah two
well it's it yeah
uh
it up yes this
that's a proper a marketing
which in fact is that i i i not automatic speech a lost to
a just just the step
for each the
in V but feel that way fish
if you can see here
let us denote by P of and is my
it would be very computationally efficient to compute a in the class that way
detection
we can take advantage of the i are gonna a kind of of this matter
and we can uh a process that it is yeah
this um
in this way
uh so here
B are not that sign
set
point
vector
is not the issue for a high number of do
so we can come
uh
so uh
P
and market
i simply multiplying
is a a a a a a a vector of the proper five
element by element with a vector or so
yeah these not means element by element multiplication between
these two but
as we can see
uh
the classical for more sound but they simple color the content
uh or what to make five
recently recently we proposed a soul call member
uh pop up to an the final projection of what
which
uh face into account not only the content
factors bar
that's that's study of these fact
and most important than
and uh
besides the fact that used are going to achieve
but that
performance in at instant
guess score that is that egg are i
it is more computation
efficient because
if you
take a look at C
uh
i
we need to compute or is the first
and N
the first
column
and to use
the out that form the previous
it that nation and and do the same
form for this map
this can be a a you know that if you should manner of my
computing or needs the first column and the first
which is not the case here
uh uh
you you the
it's uh
each of which you more plates
the uh
a
is that a on
uh medical many complex
that
patient
addition
uh there a class a lot of loading and our proposed a
of course
uh
yep and each becomes more smaller back and when we is the projection the
and uh
the next step
but what ones
which was to develop a of a step size
fashion for this
member in
uh uh how you
we we i here
uh uh it's a eight
and
again it is the step size mean
and is the we know
always
we have a complete think requirements
when we have to choose
the step size parameter because we have to compromise between combat
and misadjustment well
uh
the bit rate in
the double talk of who's that so
the you know that two
uh let's see
compromise is used the
and for out it yeah
it would be nice to use a body a step size the
and uh we start a lot development
by rewriting and it will be
this for a
uh using
uh a to use force the
step side
as as you can see
from these two
creations from this plot but it
if
we use
uh is the same but for all
and i and
we can get a each step size are working
which is
you can here
now
if we take a look
at a
a a posteriori a vector or not you are here a the which are you bring yeah
these these easy to uh what they no relation in these two that
using these two and a
a a of the seattle
now we can see is a relation and if we we remember that to the basic i
as you can find projection on reading once to chance and
be a posteriori here
the core we should
slides seas
assuming what was it
man
not well
we will get
a simple solution
which means that all the step size should be about one
unfortunately fortunately this holds already means absence of the nodes because is you remember from the
for
a
in the echo cancellation problem
we do not want to guess so
yeah
yeah are that if you don't by to recover is that near end signal
from these and so we modify
quantization
and we it i
uh
this is the
calculation that
that
then
we take the might or might have a
dictation
and the we get the
for
as a a a a a step size
of formal a like this
which yeah we have
the the elements of C L vector a
since is this is a very of but it is easy to estimate is this five
but unfortunately the problem but it mice
the estimate of the power
well of the near and
uh as the problems comes from the fact that the near end signal is in fact a combination P the
back noise and then you in speech
you when is two pass are uncorrelated heat so we can write as this relation
and even if we would basically to make this part and silence as for example
it is difficult to have an expression for this
uh
i
so
you know that to solve this problem
i i think that yeah that's that echo cancellation a configuration
so uh
we can express as you circulation in better
of
expectation
one and that of our
and now we will use a very strong assumption
which is that is yeah now if you that has come back somehow list and uh
get
in this case if if you make a a a a a a is this the assumption of this relation
can me
really then
axes
of course
as is this is a strong assumption because the
i'm i'm for example is the beginning of the adaptation one but i
station
it may not hold by
see E my the simulation that
so uh
oh performance a out of the fine
right would
uh uh most important
if we take a look at these the relation to this estimate all this
signal
all this uh measure are available because we only use
the signals
from the that if you that that that that is that i know and
it's all
oh from this point of view it would be a dark
a the solution
so the answer to our problem is
a state
step size of a space
step size
which look like to use of course
i i can be made
in the same or
say say that
and the finally
uh
we do show some uh
simulation results presented
you know a network echo cancellation problem
with a few that a lack of five and
the the wave actions
um the input will be you do white gaussian noise of speech
and a the the performance measure
a a a a a of the normalized means alignment or a according loss
a
and we will compare three at of buttons
our proposed or the last one
and
it's a uh uh a fixed stepsize action
uh uh as you can do these up here we do not mention anything about the alternate an eight five
you at can be we can was any what what's an eight back what
for
i
now uh you know uh i the experiment which is their i the elements of what in it's
what what and fact factor
was also presented by a monaural
before
uh in as
our simulation they'd
the bible
i mean
but is also
the the trans
and we compare
uh is this struggling in with
is that a have the step size
non proposed at that should the action
which is
uh
or they need to remove the
for puts an eight might from these
the ah
in the first
speed man
we compare the variable step that should with a fixed at signs
that's and was to
a fighting for the step size as we can see
uh as the body of the step size that's and combat almost as fast as
the
each step side that's start with a lot
size
but achieve a much lower miss i'm and close
and that
situation
in this case we use the
an input signal
uh
white gaussian noise
then
we uh are an snr all
uh
assuming that uh
try
patient the mean that
relation
and we compare our algorithm with a
none of course but may action
we can see that
i'll hope of that
is expected somehow
a of problems
a class L
action
in that of well convergence it
or that just
this alignment
and finally
uh uh you know that what looks and i not
uh
we compare at all
the three hour
and uh we see that for this point of view from that was to double a point of
our our that it's
much more what was as compare
yeah
at least is uh a non of a step size
yeah we use a a simple get about that that
a vector just two
and
the adaptation
double talk
i in it is a a a a few conclusion
so we propose
this out that point text of for echo cancellation
first
we can take advantage from a its computation of complex
T
which is low as from that is a class
second
we think that is important that the value of a step side
for
somehow somehow not about made
this means that we do not use any additional power uh
i'm sure one by one
and
we also note this is that
uh
these more what used to
uh near end signal evaluation
and most important to double
thank you very much earlier
for that
well we do you have some time for
i if anybody like
using again
you
presentation presentation just them curious about two
the step size it becomes negative when the values of the
design a value is a lot to than that of the there
yeah
so
in this problem
okay
this is a very nice for
because it we ski but it to meet some practical issues yeah
it is a recommended fast
to at
a small positive constant yeah
just to avoid you bytes
at most important
we should they is the absolute but
this a three star is then
the
but but what i
too much
but
if
inking your simulations used in order
a projection order
for for for yes
and is the method sense to the project no
no we just to choose this value
it's a for complex
but
we try was my
projection of
you have
and in your a personal experience
what projection order do you find is really the the right
and it to be working in
for for
uh
i think that uh to three we well we should not to take a larger than eight
that
but also because of the complexity issue you by that you for this these some uh
that's a diffusion map for completely because in a fine as the fine projection of what you
so main problem is how to invest
the out
i i have a project the higher projection of but we been blind or if you a quotation my
recently the as far as i
no was that are some more efficient manner
for example
you you all
because the models
i
or the might be end
variation of by
well also a
which she uh if you lead that for this operations so we can even go higher
a project
and other question
i think i have the opportunity to last one um
a more general question concerning the difference between acoustic echo cancellation
and network
and
could you clarify which
i think you first results at least as in the network
sure
yes
a the was the
so is the star
a point of
two and eight type of that if you that
oh
for a network that for consideration and respect to
that form but that's
send send you because of this i
of the
because i buy it can fall you by room acoustic a more cancellation if we properly choose
the proposed to eight five
this was that he's and defines the chose of the I the nlms out
which is
somehow of a host to this
ask
part think the
of course the
we can use for example a low as far as and uh as i
we proposed a few
a one year ago goes as the sparseness control coding which can
a beep on uh
well efficient as compared to ipnlms mess if we know as this from miss what it's to make some the
smell
so we is important to choose the proposed
well that's great thank you very much and