Thanks for the offer Marcel, appreciated. I'm getting on quite well with Python at present, albeit slowly, so I'll take up your offer if I run into some insurmountable difficulty there. The Python approach offers tremendous flexibility and power but its a fairly steep learning curve for me as I've never used anything similar (like Matlab/Octave) in the past. Very rewarding though to try out stuff and get effectively instant results, that's the beauty of an interpreted language.
I just went to the site, it seems to be running fine. There is an option to down load the source code so think you could run it locally. Let me know if you need it.
But if you have it up and running in Python on don’t mind not have the GUI that is fine. I’ve done very light GUI tasks using Jupyter Widgets in notebooks https://ipywidgets.readthedocs.io/en/latest/ and full professional applications using TraitsUI https://docs.enthought.com/traitsui/
But if you have it up and running in Python on don’t mind not have the GUI that is fine. I’ve done very light GUI tasks using Jupyter Widgets in notebooks https://ipywidgets.readthedocs.io/en/latest/ and full professional applications using TraitsUI https://docs.enthought.com/traitsui/
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Having the 2XOS code working the next step up for me is higher OS rates. The SAA7220 4XOS chip does a single stage upsample from 44.1 to 176.4k but in general its understood that the lowest computational complexity arises from using multiple stages (i.e. for 4X, cascade two 2X sections). The NPC and Yamaha digital filter chips use multiple 2X stages to achieve 8X, e.g. YM3414 pdf. Running 4X and above necessitates either an MCLK signal on I2S or a PLL when using a serial input DAC to get a sufficiently high frequency BCK, or alternatively use a parallel input DAC - this one looks fairly good : HI5741
Malcolm Hawksford has a relevant paper covering the tradeoffs of #stages versus computational complexity in upsampling filters, attached. Seems he likes to use half-band filters for the savings in multiplies however I'm not a fan as they allow aliasing. They do have the interesting property that all the original samples are preserved so some audiophiles might consider them the nearest thing to 'bitperfect' filters.
Incidentally Tfilter designer seems to be running fine again for me, however I'm now fully converted to using JupyterLab for FIR filter design.
Malcolm Hawksford has a relevant paper covering the tradeoffs of #stages versus computational complexity in upsampling filters, attached. Seems he likes to use half-band filters for the savings in multiplies however I'm not a fan as they allow aliasing. They do have the interesting property that all the original samples are preserved so some audiophiles might consider them the nearest thing to 'bitperfect' filters.
Incidentally Tfilter designer seems to be running fine again for me, however I'm now fully converted to using JupyterLab for FIR filter design.