Hi,
Maybe this can be of help.
http://www.musicdsp.org/archive.php?classid=3#65
All the best
Massimiliano
Il 19/01/2011 15.56, Uli Brueggemann ha scritto:
Hi,
thanks for the answer so far.
A polyphase filter is a nice idea but it does not answer the problem.
The signal has to be
On 2011-01-19, Olli Niemitalo wrote:
Find the roots, pair the complex conjugate roots and distribute the
pairs and single real roots evenly (how exactly?) in the two filters.
Matlab at least has facilities finding roots of large polynomials.
Funny. I would have gone the transform-way. That
Hello,
a technique that allows something similar to what you are suggesting
is to use polyphase filters. The difference is that you will not
process contiguous vectors, but (for a 2-phase decomposition example)
process the even samples with one stage of the filter and the odd
samples with another
Hi,
thanks for the answer so far.
A polyphase filter is a nice idea but it does not answer the problem.
The signal has to be demultiplexed (decimated), the different streams
have to be filtered, the results must be added to get the final output
signal.
My question has a different target.
Imagine
Find the roots, pair the complex conjugate roots and distribute the
pairs and single real roots evenly (how exactly?) in the two filters.
Matlab at least has facilities finding roots of large polynomials.
-olli
On Wed, Jan 19, 2011 at 4:56 PM, Uli Brueggemann
uli.brueggem...@gmail.com wrote:
-Original Message-
From: music-dsp-boun...@music.columbia.edu
[mailto:music-dsp-boun...@music.columbia.edu] On Behalf Of Uli Brueggemann
Sent: 19 January 2011 14:56
To: A discussion list for music-related DSP
Subject: Re: [music-dsp] Factorization of filter kernels
Hi,
thanks for the answer
[mailto:music-dsp-boun...@music.columbia.edu] On Behalf Of Uli Brueggemann
Sent: 19 January 2011 14:56
To: A discussion list for music-related DSP
Subject: Re: [music-dsp] Factorization of filter kernels
Hi,
thanks for the answer so far.
A polyphase filter is a nice idea but it does not answer
I've only seen this kind of thing done on 2D signals (i.e. images),
where it is much faster to use two 1D convolution passes (one in each
dimension) than to use the much larger 2D kernel. The term I'm used to
is a separable filter. It works fine as a technique, but you are
very restricted in the
