You can look Adaptive signal processing (B Widrow and S D Stearn) or you
can find an example in Adaptive filter theory (S.Haykin)...
Hope this will help.
Moc
>Thanks guys for your suggestions....
>  yes the ALE works in this case by generating its own reference using
the
>input signal with a delay. For a delay more than 20 samples this method
>works at removing pure sinusoids and a mix of sinusoids.
>It also removes a small portion of the input signal as well.
>The trick lies in identifying the appropriate delay based on the input
>signals and the step size to avoid removing the input signal....
>Any references/papers (not asking solutions...) to identigy them would
help
>a lot....
>
>regards,
>srikanth konjeti
>
>>On Jun 2, 12:17=A0pm, Vladimir Vassilevsky <nos...@nowhere.com> wrote:
>>> maury wrote:
>>> > On Jun 1, 5:24 am, "srikanth.konj"
>>> > <srikanth.konj@n_o_s_p_a_m.gmail.com> wrote:
>>>
>>> >> a feedback signal which is a form of sinusoid or mix of sinusoids
is
>>> >> getting mixed to the speech signal. So i would like to remove the
>unwa=
>>nted
>>> >> sinusoidal signals. (here noise is sinusoid and not white noise)
>>> > I'll give you a hint, look up adaptive line enhancer (ALE). It is a
>>> > LMS-based adaptive notch filter that makes its own reference. Make a
>>> > simulation of the ALE, then run it varying the update gain and see
>the
>>> > results as a function of the update gain. Then decide if LMS-based
>>> > filters work in this application.
>>>
>>> One approach to make the LMS work in this application is to add some
>>> artificial components into the signal. Those components could be used
>as
>>> a reference for system identification. The trick is how to make it
>>> efficient without introducing the significant artifacts.
>>>
>>> Vladimir Vassilevsky
>>> DSP and Mixed Signal Design Consultanthttp://www.abvolt.com
>>
>>As long as the interference is a sinusoid, that's all that's needed.
>>The ALE will make it's own reference. Now,...... how well it works for
>>different types of signals (e.g., noise vs speech), I'm leaving up to
>>the OP to figure out when (s)he runs the simulations.
>>
>

On Jun 3, 2:41�am, "srikanth.konj"
<srikanth.konj@n_o_s_p_a_m.gmail.com> wrote:
> Thanks guys for your suggestions....
> &#4294967295; yes the ALE works in this case by generating its own reference using the
> input signal with a delay. For a delay more than 20 samples this method
> works at removing pure sinusoids and a mix of sinusoids.
> It also removes a small portion of the input signal as well.
> The trick lies in identifying the appropriate delay based on the input
> signals and the step size to avoid removing the input signal....
> Any references/papers (not asking solutions...) to identigy them would help
> a lot....
>
> regards,
> srikanth konjeti
>
>
>
>
>
> >On Jun 2, 12:17=A0pm, Vladimir Vassilevsky <nos...@nowhere.com> wrote:
> >> maury wrote:
> >> > On Jun 1, 5:24 am, "srikanth.konj"
> >> > <srikanth.konj@n_o_s_p_a_m.gmail.com> wrote:
>
> >> >> a feedback signal which is a form of sinusoid or mix of sinusoids is
> >> >> getting mixed to the speech signal. So i would like to remove the
> unwa=
> >nted
> >> >> sinusoidal signals. (here noise is sinusoid and not white noise)
> >> > I'll give you a hint, look up adaptive line enhancer (ALE). It is a
> >> > LMS-based adaptive notch filter that makes its own reference. Make a
> >> > simulation of the ALE, then run it varying the update gain and see
> the
> >> > results as a function of the update gain. Then decide if LMS-based
> >> > filters work in this application.
>
> >> One approach to make the LMS work in this application is to add some
> >> artificial components into the signal. Those components could be used
> as
> >> a reference for system identification. The trick is how to make it
> >> efficient without introducing the significant artifacts.
>
> >> Vladimir Vassilevsky
> >> DSP and Mixed Signal Design Consultanthttp://www.abvolt.com
>
> >As long as the interference is a sinusoid, that's all that's needed.
> >The ALE will make it's own reference. Now,...... how well it works for
> >different types of signals (e.g., noise vs speech), I'm leaving up to
> >the OP to figure out when (s)he runs the simulations.- Hide quoted text -
>
> - Show quoted text -

Go to http://www.google.com, then type in "adaptive line enhancer" in
the search window, then click the "SEARCH" button.

Thanks guys for your suggestions....
  yes the ALE works in this case by generating its own reference using the
input signal with a delay. For a delay more than 20 samples this method
works at removing pure sinusoids and a mix of sinusoids.
It also removes a small portion of the input signal as well.
The trick lies in identifying the appropriate delay based on the input
signals and the step size to avoid removing the input signal....
Any references/papers (not asking solutions...) to identigy them would help
a lot....

regards,
srikanth konjeti

>On Jun 2, 12:17=A0pm, Vladimir Vassilevsky <nos...@nowhere.com> wrote:
>> maury wrote:
>> > On Jun 1, 5:24 am, "srikanth.konj"
>> > <srikanth.konj@n_o_s_p_a_m.gmail.com> wrote:
>>
>> >> a feedback signal which is a form of sinusoid or mix of sinusoids is
>> >> getting mixed to the speech signal. So i would like to remove the
unwa=
>nted
>> >> sinusoidal signals. (here noise is sinusoid and not white noise)
>> > I'll give you a hint, look up adaptive line enhancer (ALE). It is a
>> > LMS-based adaptive notch filter that makes its own reference. Make a
>> > simulation of the ALE, then run it varying the update gain and see
the
>> > results as a function of the update gain. Then decide if LMS-based
>> > filters work in this application.
>>
>> One approach to make the LMS work in this application is to add some
>> artificial components into the signal. Those components could be used
as
>> a reference for system identification. The trick is how to make it
>> efficient without introducing the significant artifacts.
>>
>> Vladimir Vassilevsky
>> DSP and Mixed Signal Design Consultanthttp://www.abvolt.com
>
>As long as the interference is a sinusoid, that's all that's needed.
>The ALE will make it's own reference. Now,...... how well it works for
>different types of signals (e.g., noise vs speech), I'm leaving up to
>the OP to figure out when (s)he runs the simulations.
>

On Jun 2, 12:17&#4294967295;pm, Vladimir Vassilevsky <nos...@nowhere.com> wrote:
> maury wrote:
> > On Jun 1, 5:24 am, "srikanth.konj"
> > <srikanth.konj@n_o_s_p_a_m.gmail.com> wrote:
>
> >> a feedback signal which is a form of sinusoid or mix of sinusoids is
> >> getting mixed to the speech signal. So i would like to remove the unwanted
> >> sinusoidal signals. (here noise is sinusoid and not white noise)
> > I'll give you a hint, look up adaptive line enhancer (ALE). It is a
> > LMS-based adaptive notch filter that makes its own reference. Make a
> > simulation of the ALE, then run it varying the update gain and see the
> > results as a function of the update gain. Then decide if LMS-based
> > filters work in this application.
>
> One approach to make the LMS work in this application is to add some
> artificial components into the signal. Those components could be used as
> a reference for system identification. The trick is how to make it
> efficient without introducing the significant artifacts.
>
> Vladimir Vassilevsky
> DSP and Mixed Signal Design Consultanthttp://www.abvolt.com

As long as the interference is a sinusoid, that's all that's needed.
The ALE will make it's own reference. Now,...... how well it works for
different types of signals (e.g., noise vs speech), I'm leaving up to
the OP to figure out when (s)he runs the simulations.

maury wrote:

> On Jun 1, 5:24 am, "srikanth.konj"
> <srikanth.konj@n_o_s_p_a_m.gmail.com> wrote:
> 
>> a feedback signal which is a form of sinusoid or mix of sinusoids is
>> getting mixed to the speech signal. So i would like to remove the unwanted
>> sinusoidal signals. (here noise is sinusoid and not white noise)


> I'll give you a hint, look up adaptive line enhancer (ALE). It is a
> LMS-based adaptive notch filter that makes its own reference. Make a
> simulation of the ALE, then run it varying the update gain and see the
> results as a function of the update gain. Then decide if LMS-based
> filters work in this application.

One approach to make the LMS work in this application is to add some 
artificial components into the signal. Those components could be used as 
a reference for system identification. The trick is how to make it 
efficient without introducing the significant artifacts.

Vladimir Vassilevsky
DSP and Mixed Signal Design Consultant
http://www.abvolt.com

On Jun 1, 5:24�am, "srikanth.konj"
<srikanth.konj@n_o_s_p_a_m.gmail.com> wrote:
> Hi All,
>
> &#4294967295; my problem statement is as follows
>
> Prob:
> =====
> &#4294967295; a feedback signal which is a form of sinusoid or mix of sinusoids is
> getting mixed to the speech signal. So i would like to remove the unwanted
> sinusoidal signals. (here noise is sinusoid and not white noise)
>
> Experiment:
> ===========
> Trying to use LMS to remove the sunusoid. My understanding is that
> LMS will initally try to autocorrelate the input signal to itself and the
> coefficients will adapt as a bandpass filter with the center as the
> sinusoidal frequency. So the error here is the speech signal after
> subtraction with the desired signal.
>
> Question:
> =========
> 1. My intention is to increase the error signal (speech) so how to select
> the step size (convergence index)
> 2. most of the theories try to reduce the mean square error and decide on
> the step sizes. Will the same work here
> 3. How much delay is necessary to decrease the correlation of the speech
> signal at the same time maintain the correlation of the inherent sinusoid
> signal (noise here)

Since this is an experiment, do it as an experiment.
I'll give you a hint, look up adaptive line enhancer (ALE). It is a
LMS-based adaptive notch filter that makes its own reference. Make a
simulation of the ALE, then run it varying the update gain and see the
results as a function of the update gain. Then decide if LMS-based
filters work in this application. If you decide they do, then what do
you need to do to optimize the results? What are the pitfalls? What
are the advantages? What are the disadvantages? Under what
circumstances would you use it? When would you not use it?

Maurice Givens

1. What you are asking for is called "howling suppression".

2. LMS won't work. Because you don't have clean reference to optimize 
with respect to.

3. What you need is adaptive notch filter. There are tons of boring 
books and articles to read.

4. This is complicated area and very commertial also. Amateur approach 
is not feasible. Don't expect a solution for free.


Vladimir Vassilevsky
DSP and Mixed Signal Design Consultant
http://www.abvolt.com




srikanth.konj wrote:
> Hi All,
> 
>   my problem statement is as follows
> 
> Prob:
> =====
>   a feedback signal which is a form of sinusoid or mix of sinusoids is
> getting mixed to the speech signal. So i would like to remove the unwanted
> sinusoidal signals. (here noise is sinusoid and not white noise)
> 
> Experiment:
> ===========
> Trying to use LMS to remove the sunusoid. My understanding is that
> LMS will initally try to autocorrelate the input signal to itself and the
> coefficients will adapt as a bandpass filter with the center as the
> sinusoidal frequency. So the error here is the speech signal after
> subtraction with the desired signal.
> 
> Question:
> =========
> 1. My intention is to increase the error signal (speech) so how to select
> the step size (convergence index)
> 2. most of the theories try to reduce the mean square error and decide on
> the step sizes. Will the same work here
> 3. How much delay is necessary to decrease the correlation of the speech
> signal at the same time maintain the correlation of the inherent sinusoid
> signal (noise here)
> 
> 
> 

Hi All,

  my problem statement is as follows

Prob:
=====
  a feedback signal which is a form of sinusoid or mix of sinusoids is
getting mixed to the speech signal. So i would like to remove the unwanted
sinusoidal signals. (here noise is sinusoid and not white noise)

Experiment:
===========
Trying to use LMS to remove the sunusoid. My understanding is that
LMS will initally try to autocorrelate the input signal to itself and the
coefficients will adapt as a bandpass filter with the center as the
sinusoidal frequency. So the error here is the speech signal after
subtraction with the desired signal.

Question:
=========
1. My intention is to increase the error signal (speech) so how to select
the step size (convergence index)
2. most of the theories try to reduce the mean square error and decide on
the step sizes. Will the same work here
3. How much delay is necessary to decrease the correlation of the speech
signal at the same time maintain the correlation of the inherent sinusoid
signal (noise here)