I have to implement three filters in C++. An IIR, FIR and
COMBINED(basically a sum of the two). I have been given the formulae and
its fairly simple (I think) but I know little about filters and would like
to be able to test them against something accurate (a good data set). Is
there something [free] that I can use to input my formulae and obtain a
good test data set against which I can test my implementation. 

Oddly (from what I have been reading here and elsewhere) the FIR filter
contains terms in Y. Is this reasonable?

any help appreciated.

>I have to implement three filters in C++. An IIR, FIR and
>COMBINED(basically a sum of the two). I have been given the formulae and
>its fairly simple (I think) but I know little about filters and would
like
>to be able to test them against something accurate (a good data set). Is
>there something [free] that I can use to input my formulae and obtain a
>good test data set against which I can test my implementation. 
>
>Oddly (from what I have been reading here and elsewhere) the FIR filter
>contains terms in Y. Is this reasonable?
>
>any help appreciated.
>
>
>

My suggestion is to simulate it in Matlab using a noise signal as the
filters input.  Create any kind of noise signal
(white/pink/gaussian/whatever) and make sure you only do this step once! 
Every time you call the rand() function you'll get a new data set, so
you'll need to save you're workspace for repeatable results.

Then put the coefficients ("a" and "b" terms) into the filter() function
and BAM you're done.  If you have a series of filters, you also convolve
the coefficients to get the cascaded response usning conv().  Keep in mind
this is done easily with floating point arithmetic.  If it's fixed point
then you'll have to be much more crafty with you're Matlab code.

Unfortunatley there is no standard data set out there for verification
purposes, you'll have to create your own.

Jeff

On Feb 20, 3:13 pm, "tismealso" <midgley...@hotmail.com> wrote:
> I have to implement three filters in C++. An IIR, FIR and
> COMBINED(basically a sum of the two). I have been given the formulae and
> its fairly simple (I think) but I know little about filters and would like
> to be able to test them against something accurate (a good data set). Is
> there something [free] that I can use to input my formulae and obtain a
> good test data set against which I can test my implementation.
>
> Oddly (from what I have been reading here and elsewhere) the FIR filter
> contains terms in Y. Is this reasonable?
>
> any help appreciated.

Hello,

If in your doubt, Y is output, then this is not reasonable since the
filter is no more an FIR filter but an IIR filter. Y terms in filter
mean feedback.

Where do you get these desine formula?

Do you know with certaintly what this frequency respons should be? If
no, then checking result will be hard. If yes, do FFT of impulse
respons and see if match is obtained. But first find out why Y terms
nonzero. Make size of FFT big enough to see detail to make match with
expected. If FIR, use no window on impulse respons.

Regards,

Kamar Ruptan
DSP Guru

tismealso wrote:
> I have to implement three filters in C++. An IIR, FIR and
> COMBINED(basically a sum of the two).

Perhaps you meant recursive, transversal and combined?

> I have been given the formulae and
> its fairly simple (I think) but I know little about filters and would like
> to be able to test them against something accurate (a good data set). Is
> there something [free] that I can use to input my formulae and obtain a
> good test data set against which I can test my implementation.

You can use Scilab or Octave (free Matlab-like interpreters). As
others have written, you can take noise or an impulse as input and see
what comes out. The impulse response is (theoretically) sufficient to
check your filter. However, programming errors might not show up in
the impulse response, so random signals filtering is a good double-
check for your implementation.


>
> Oddly (from what I have been reading here and elsewhere) the FIR filter
> contains terms in Y. Is this reasonable?

Assuming that Y is the output, then it is possible, but unlikely.
Recursive FIR filter do exist but are probably not on the agenda for
students meeting filters for the first time (but then again, they
might be). An example of a recursive FIR filter is the "boxcar"
filter, which just averages over a number of samples (y is output, x
is input):

y[n] = 1/N (x[n] + x[n-1] + ... + x[n-(N-1)] ).

Its recursive form is

y[n] = y[n-1] + 1/N (x[n] - x[n-N]).

>
> any help appreciated.

Regards,
Andor

DSPGURU wrote:
> If in your doubt, Y is output, then this is not reasonable since the
> filter is no more an FIR filter but an IIR filter. Y terms in filter
> mean feedback.

y[n] = x[n] + y[n-1] - x[n-N]

This has feedback, but it's FIR.  While IIR's require feedback, feedback 
does not necessarily imply IIR.

-- 
Jim Thomas            Principal Applications Engineer  Bittware, Inc
j...@bittware.com  http://www.bittware.com    (603) 226-0404 x536
The sooner you get behind, the more time you'll have to catch up

>On Feb 20, 3:13 pm, "tismealso" <midgley...@hotmail.com> wrote:
>> I have to implement three filters in C++. An IIR, FIR and
>> COMBINED(basically a sum of the two). I have been given the formulae
and
>> its fairly simple (I think) but I know little about filters and would
like>
>> to be able to test them against something accurate (a good data set).
Is
>> there something [free] that I can use to input my formulae and obtain
a
>> good test data set against which I can test my implementation.
>>
>> Oddly (from what I have been reading here and elsewhere) the FIR
filter
>> contains terms in Y. Is this reasonable?
>>
>> any help appreciated.
>
>Hello,
>
>If in your doubt, Y is output, then this is not reasonable since the
>filter is no more an FIR filter but an IIR filter. Y terms in filter
>mean feedback.
>

I suspect you are right. 

>Where do you get these desine formula?
>

from customer requirements, this is why I havent posted them since they
are in confidence. but I believe they are asking for something they dont
want. 

>Do you know with certaintly what this frequency respons should be? If
>no, then checking result will be hard. If yes, do FFT of impulse
>respons and see if match is obtained. But first find out why Y terms
>nonzero. Make size of FFT big enough to see detail to make match with
>expected. If FIR, use no window on impulse respons.
>

thank you, it is more a standard curve against a pulse input or a step
input that I am looking for. something I can prove the code against so
matlab alike. ( I dont have matlab and we wont be buying it for this task
alone). 

>Regards,
>
>Kamar Ruptan
>DSP Guru
>

thanks for your help. 

>tismealso wrote:
>> I have to implement three filters in C++. An IIR, FIR and
>> COMBINED(basically a sum of the two).
>
>Perhaps you meant recursive, transversal and combined?
>
>> I have been given the formulae and
>> its fairly simple (I think) but I know little about filters and would
like
>> to be able to test them against something accurate (a good data set).
Is
>> there something [free] that I can use to input my formulae and obtain
a
>> good test data set against which I can test my implementation.
>
>You can use Scilab or Octave (free Matlab-like interpreters). As
>others have written, you can take noise or an impulse as input and see
>what comes out. The impulse response is (theoretically) sufficient to
>check your filter. However, programming errors might not show up in
>the impulse response, so random signals filtering is a good double-
>check for your implementation.
>
>

Excellent that is what I am looking for, thankyou very much. 

>>
>> Oddly (from what I have been reading here and elsewhere) the FIR
filter
>> contains terms in Y. Is this reasonable?
>
>Assuming that Y is the output, then it is possible, but unlikely.
>Recursive FIR filter do exist but are probably not on the agenda for
>students meeting filters for the first time (but then again, they
>might be). An example of a recursive FIR filter is the "boxcar"
>filter, which just averages over a number of samples (y is output, x
>is input):
>

I didnt mean to mislead anyone, I am a software engineer and this is a
professional exercise, its a small part of a much bigger project, the
filters will have been designed ONCE but I think there is a mistake
because these filters dont need to do anything other than noise supression
and a certain lead/lag (many filters - same algorithm). 

>y[n] = 1/N (x[n] + x[n-1] + ... + x[n-(N-1)] ).
>
>Its recursive form is
>
>y[n] = y[n-1] + 1/N (x[n] - x[n-N]).
>
>>
>> any help appreciated.
>
>Regards,
>Andor
>
>
thanks again,

On Feb 21, 9:44 am, Jim Thomas <jtho...@bittware.com> wrote:
> DSPGURU wrote:
> > If in your doubt, Y is output, then this is not reasonable since the
> > filter is no more an FIR filter but an IIR filter. Y terms in filter
> > mean feedback.
>
> y[n] = x[n] + y[n-1] - x[n-N]
>
> This has feedback, but it's FIR.  While IIR's require feedback, feedback
> does not necessarily imply IIR.
>
> --
> Jim Thomas            Principal Applications Engineer  Bittware, Inc
> jtho...@bittware.com  http://www.bittware.com   (603) 226-0404 x536
> The sooner you get behind, the more time you'll have to catch up

Hello James,

You are correct that you can take FIR filter and add cancelling pairs
of poles and zeros to make it have feedback. Rarely would you ever
want to, rarely would you want such a filter from general filter
design program. Generally (not your example) it makes filter take more
computations, makes some fix-point implementation harder.  In special
case of boxcar function you present, since filter has pole on unit
circle, having the filter work right depends on things that one would
need to be knowledgable off or have luck to avoid. If you break filter
into two sections, first implementing denominator, second implementing
numerator, filter can only work with non-saturating fix-point
implementation, not floating-point, not saturating fix-point, because
first section is unstable.  If program designed your filter and got 2
ones in denominator, exactly, but numerator coefficients not exactly
same (1 or not, but not 0), then filter not stable. If all cancelling
poles and zeros not match exactly filter turns to IIR. Your filter is
special design for boxcar filter alone or as use in CIC filter, it
would not be expected from general filter design program. My comments
about 'same' numbers obviously ignore appropriate signs; apply as
appropriate.

Original poster should not expect feedback terms from general filter
design program that is to design FIR filter. If so, he should get
money back.

(got English help from my mate Roopnath, understand better?)

Regards,

Kamar Ruptan
DSP Guru