[SciPy-Dev] Overlap-add convolution implementation

[Todd]

The heuristic I have right now works fine.  It is a negligible fraction of
overall function run time for the large arrays where this approach makes
sense to use in the first place.  I am not sure whether it is optimal, but
it is certainly usable.  Still, I plan to allow people to specify a block
size, which would skip that step completely.

On Sat, Jun 15, 2019 at 1:48 PM Freddy Rietdijk <freddyrietdijk at fridh.nl>
wrote:

> I suppose whether it can be part of `convolve()` depends on whether any
> additional options are needed. Unless a working heuristic is found the
> block size will have to be a parameter. In case convolution with a variant
> signal should be supported the hop size should be considered as well. In my
> experience you will want to use an iterative solution though when doing
> convolution with a variant signal.
>
> On Sat, Jun 15, 2019 at 7:22 PM <3ukip0s02 at sneakemail.com> wrote:
>
>>
>>
>> On Sat, Jun 15, 2019 at 12:01 PM Todd wrote:
>>
>>> I am currently working an an implementation of overlap-add convolution
>>> [0].  I have a 1D implementation working and I am going to start
>>> expanding
>>> it to ND.  Overlap-add convolution can provide significant performance
>>> benefits over FFT-based convolution.  Does this make sense for scipy?
>>>
>>
>> Yeah!
>>
>>
>>> First is the name.  We have "convolve" and "fftconvolve" already.  A few
>>> options:
>>>
>>> overall_add_conv
>>> oaconvolve
>>> oadconvolve
>>>
>>
>> fftconvolve has been folded into convolve and can either be selected
>> manually, or will automatically choose direct convolution, whichever is
>> likely faster.
>>
>> Does it make sense for the OLA implementation to also be added as a
>> convolve() option, using the "method" parameter?
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