Ok, earplug~ 0.3.0 is ready: https://github.com/pd-externals/earplug <https://github.com/pd-externals/earplug>
Lucas (or any Windows dev), if you have time, could you make Windows builds for deken? Then Joao can give it a try... > On Jun 2, 2021, at 5:04 PM, Dan Wilcox <[email protected]> wrote: > > Nothing too major. > > We haven't made a changelog yet but the changes in the current main branch > form the last 0.2.1 tag (2013) are: > > * update to build via pd-lib-builder > * loading the dataset txt file is now optional (needs double-checking) > * fixed hang when dataset file is not loadable > * post header only once on load and include version > * avoid crashes by making sure that the "dsp" method cannot be called from > the patch > > Some future optimizations in the pipeline are: > https://github.com/pd-externals/earplug/pulls > <https://github.com/pd-externals/earplug/pulls> > > I will try to add a changelog and new stable 0.2.2 tag tonight. You could > then bug a Windows dev to make a build for deken... > > When we review the other optimizations, they would go into a 0.3.0 version. > >> On Jun 2, 2021, at 3:56 PM, [email protected] >> <mailto:[email protected]> wrote: >> >> Message: 3 >> Date: Wed, 2 Jun 2021 13:33:11 +0100 >> From: Jo?o Pais <[email protected] <mailto:[email protected]>> >> Cc: Pd-List <[email protected] <mailto:[email protected]>> >> Subject: Re: [PD] Binaural w32? >> Message-ID: <[email protected] >> <mailto:[email protected]>> >> Content-Type: text/plain; charset=windows-1252; format=flowed >> >> that is nice - although I can't really build anything in windows on my >> own. what are the improvements compared to the 2009 version? >> >> >>> I updated the earplug~ external to build using pd-lin-builder, so it >>> should be easy to build on Windows: >>> >>> https://github.com/pd-externals/earplug/tree/main >>> <https://github.com/pd-externals/earplug/tree/main> >>> <https://github.com/pd-externals/earplug/tree/main >>> <https://github.com/pd-externals/earplug/tree/main>> >>> >>> It's not the best but we use it for a project at work. It can use some >>> additional optimizations and the dataset is for 44.1k so you are in >>> luck. For our project, we are likely to introduce resampling for other >>> sample rates so it's more accurate beyond 44.1k... -------- Dan Wilcox @danomatika <http://twitter.com/danomatika> danomatika.com <http://danomatika.com/> robotcowboy.com <http://robotcowboy.com/>
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