Loopy Pro: Create music, your way.

What is Loopy Pro?Loopy Pro is a powerful, flexible, and intuitive live looper, sampler, clip launcher and DAW for iPhone and iPad. At its core, it allows you to record and layer sounds in real-time to create complex musical arrangements. But it doesn’t stop there—Loopy Pro offers advanced tools to customize your workflow, build dynamic performance setups, and create a seamless connection between instruments, effects, and external gear.

Use it for live looping, sequencing, arranging, mixing, and much more. Whether you're a live performer, a producer, or just experimenting with sound, Loopy Pro helps you take control of your creative process.

Download on the App Store

Loopy Pro is your all-in-one musical toolkit. Try it for free today.

High End Hardware Emulations for BYOD IOS

124

Comments

  • @ninobeatz said:

    @bangzero said:
    i've been intrigued lately by the sound of guitar pedals based on the Neve 1073 mic pre, the Lauren Audio Mustang and JHS Crayon. so i got the AnalogXAi AMS Vintage Neve 1073 profile (on sale!) imho, it's quite good. the saturation is nice and gritty, and it has a rich fuzz tone when pushed hard. it is dark sounding & works as an amp replacement without a cab IR. i like it as a virtual pedal in front of a bright, clean amp sim. i'm just a hacker tho. any real electric guitar folks with thoughts on this one? & thanks to the OP for the AnalogXAi link!

    Does it beat the 1073 we have on iOS from Red Rock Sound? I mean..that one is $5 and an emulation of the entire unit

    I play Electric but haven't tried this out yet. I download the free pack from Analogaxi but haven't figured out how to load up the profiles.

  • edited February 16

    @Tones4Christ heres a screenshot of the setup in BYOD. On the GuitarML module where it says “Blues Jr” tap there and it’ll have a little menu, hit “Custom” at the bottom and it’ll bring up the file picker. The AnalogxAI stuff should come with some JSON files, those will be what you load into the GuitarML module. Also I try to run at -18db before I hit the first clean gain in BYOD but idk if that matters.

  • @Fingolfinzz said:
    @Tones4Christ heres a screenshot of the setup in BYOD. On the GuitarML module where it says “Blues Jr” tap there and it’ll have a little menu, hit “Custom” at the bottom and it’ll bring up the file picker. The AnalogxAI stuff should come with some JSON files, those will be what you load into the GuitarML module. Also I try to run at -18db before I hit the first clean gain in BYOD but idk if that matters.

    Sweet! Can't wait to try it out.
    Right now I'm using the current Beta of BYOD and I totally love it!!!! There are a few new pedals that I definitely love!!! Added it to my pedalboard Setup for sure!

    Just wish it had more delays though.

  • I’m testing stuff out now but I’ll have some profiles I’ll share on here in a few days of some of my various gear

  • @Fingolfinzz said:
    I’m testing stuff out now but I’ll have some profiles I’ll share on here in a few days of some of my various gear

    So awesome!!!!

  • Here’s a couple so far. Still working on it but it’s coming along. Not super exciting, one is my werkstatt filter pushed a bit hard and the other one is less exciting, it’s a plug-in capture of acustica audio pink and pumpkin with the tape setting pushed really hot. Let me know how they sound, the accuracy printout showed they were a little off but not too much. More to come!

  • @HotStrange said:
    While we’re here has anyone tried the IAPs in BYOD? Specifically the Filters? I’m very interested but wondering which filters it comes with and if it’s worth it considering I already have Volcano and JAF.

    Some info here. The filters do look interesting https://chowdsp.com/products/byod-add-ons/store.html

  • @Icoustik said:

    @bangzero said:

    @Icoustik said:
    Do make sure you set BYOD´s (assuming you’re using that) oversampling to 1x, and turn off the sample rate correction filter in the settings on the GuitarML module, for optimal sound 👍

    thanks. knew about 1x os, but the filter was on. what does that do?

    It just muddies the sound and wastes resources.
    Try turning on linear phase in the same menu as well and see if you can hear that in the Neve

    I’m interested in the Analogxai stuff but confused with setup. The dev has said on another forum that all his stuff is at 44.1 so oversampling should be used if using anything else. iOS is fixed at 48 I think…? Is that what the filter part you mention is to do with? Couldn’t see the linear phase mentioned in any menus…
    I know some users on the forum said they couldn’t use it as they lost too much high frequency info due to sample rate issues/oversampling.
    I’m not tech enough to fully understand all this if anyone can explain in layman’s?
    It may be that you aren’t using in iOS…
    Cheers

  • edited April 1

    @Zerozerozero said:

    @Icoustik said:

    @bangzero said:

    @Icoustik said:
    Do make sure you set BYOD´s (assuming you’re using that) oversampling to 1x, and turn off the sample rate correction filter in the settings on the GuitarML module, for optimal sound 👍

    thanks. knew about 1x os, but the filter was on. what does that do?

    It just muddies the sound and wastes resources.
    Try turning on linear phase in the same menu as well and see if you can hear that in the Neve

    I’m interested in the Analogxai stuff but confused with setup. The dev has said on another forum that all his stuff is at 44.1 so oversampling should be used if using anything else. iOS is fixed at 48 I think…? Is that what the filter part you mention is to do with? Couldn’t see the linear phase mentioned in any menus…
    I know some users on the forum said they couldn’t use it as they lost too much high frequency info due to sample rate issues/oversampling.
    I’m not tech enough to fully understand all this if anyone can explain in layman’s?
    It may be that you aren’t using in iOS…
    Cheers

    I make GuitarML profiles as well, when you are sampling the hardware (that’s on my end, not yours) I have to sample it in 44.1k due to limitations. Once the profile is made though, the user can use that JSON file in 48k. Linear phase is towards the bottom of the oversampling menu. If there is high end loss, it’s due to the sampling process or the hardware, BYOD should only have a drop in the sub range. It’s a known bug but it’s out there. I actually made some tutorials on how to do it on my YouTube page. It’s the first couple of videos if you need a visual on the setup https://youtube.com/@GrittyToolsFromChamber93?feature=shared

  • edited April 1

    Sorry for being newb like about this but how would these compare to ddmf’s neve 1073 clone? And then to something like a golden audio 1073 or warm hardware clone?

    Also how would acustica audio compare to those?

    Anyone with experience of real hardware pres like 1073 and clones and the plugins could give me a bit of a rundown? I’m really interested for vocals and acoustic instruments,

    I read gear space and get tempted into the hardware but I’m hoping plugins can do the same. I use guitar amp plugins so perhaps the same results can be got for other things like vocals etc.

  • @wingwizard said:
    Sorry for being newb like about this but how would these compare to ddmf’s neve 1073 clone? And then to something like a golden audio 1073 or warm hardware clone?

    I read gear space and get tempted into the hardware but I’m hoping plugins can do the same. I use guitar amp plugins so perhaps the same results can be got for other things like vocals etc.

    For things like saturation, thd, distortion, etc, algorithms are dead to me for the duties. I used a few of the profiles and then got so obsessed that I’m sampling hardware myself and doing the machine learning bits. It is a bit limited because knob captures take a very long time so I have just been doing snapshots, but my idea was just to capture the vibe of the hardware cos that’s what I’m always seeking out with the plugins and I am achieving that quite well. I do have several freebies on my store page if you want to check out how it sounds compared to plugins.

  • @Fingolfinzz said:

    @wingwizard said:
    Sorry for being newb like about this but how would these compare to ddmf’s neve 1073 clone? And then to something like a golden audio 1073 or warm hardware clone?

    I read gear space and get tempted into the hardware but I’m hoping plugins can do the same. I use guitar amp plugins so perhaps the same results can be got for other things like vocals etc.

    For things like saturation, thd, distortion, etc, algorithms are dead to me for the duties. I used a few of the profiles and then got so obsessed that I’m sampling hardware myself and doing the machine learning bits. It is a bit limited because knob captures take a very long time so I have just been doing snapshots, but my idea was just to capture the vibe of the hardware cos that’s what I’m always seeking out with the plugins and I am achieving that quite well. I do have several freebies on my store page if you want to check out how it sounds compared to plugins.

    Thank you I will do that and have a read back in this thread. How do you feel both ddmf or algorithmic stuff, and then the approach you describe which I’m understanding as kind of like impulse réponse (sorry if it’s not comparable) compare with actual physical hardware?

  • @wingwizard said:

    @Fingolfinzz said:

    @wingwizard said:
    Sorry for being newb like about this but how would these compare to ddmf’s neve 1073 clone? And then to something like a golden audio 1073 or warm hardware clone?

    I read gear space and get tempted into the hardware but I’m hoping plugins can do the same. I use guitar amp plugins so perhaps the same results can be got for other things like vocals etc.

    For things like saturation, thd, distortion, etc, algorithms are dead to me for the duties. I used a few of the profiles and then got so obsessed that I’m sampling hardware myself and doing the machine learning bits. It is a bit limited because knob captures take a very long time so I have just been doing snapshots, but my idea was just to capture the vibe of the hardware cos that’s what I’m always seeking out with the plugins and I am achieving that quite well. I do have several freebies on my store page if you want to check out how it sounds compared to plugins.

    Thank you I will do that and have a read back in this thread. How do you feel both ddmf or algorithmic stuff, and then the approach you describe which I’m understanding as kind of like impulse réponse (sorry if it’s not comparable) compare with actual physical hardware?

    I do think DDMF has very good dsp and algorithms do get close but the snapshots just have the edge to my ears get the nonlinear aspects better than algorithms can. That somewhat randomness when you push hardware and get that break is just really difficult to perfect with code alone, at least to my knowledge. I’m in no way an expert when it comes to dsp coding and couldn’t write anything worth while in it.

    Yeah, the GuitarML, from the background stuff I have read, is doing something similar to an impulse response. It involves using a before and after and comparing the two and learning from the nonlinear behavior the after file. Not quite an impulse though cos it’s much longer than an impulse file, I usually use about five minutes of audio run clean and then run through the hardware setting I want, then let the machine learning to its thing and then tweak things here and there until my readouts are as close to full accuracy

  • edited April 2

    @Fingolfinzz said:

    @wingwizard said:

    @Fingolfinzz said:

    @wingwizard said:
    Sorry for being newb like about this but how would these compare to ddmf’s neve 1073 clone? And then to something like a golden audio 1073 or warm hardware clone?

    I read gear space and get tempted into the hardware but I’m hoping plugins can do the same. I use guitar amp plugins so perhaps the same results can be got for other things like vocals etc.

    For things like saturation, thd, distortion, etc, algorithms are dead to me for the duties. I used a few of the profiles and then got so obsessed that I’m sampling hardware myself and doing the machine learning bits. It is a bit limited because knob captures take a very long time so I have just been doing snapshots, but my idea was just to capture the vibe of the hardware cos that’s what I’m always seeking out with the plugins and I am achieving that quite well. I do have several freebies on my store page if you want to check out how it sounds compared to plugins.

    Thank you I will do that and have a read back in this thread. How do you feel both ddmf or algorithmic stuff, and then the approach you describe which I’m understanding as kind of like impulse réponse (sorry if it’s not comparable) compare with actual physical hardware?

    I do think DDMF has very good dsp and algorithms do get close but the snapshots just have the edge to my ears get the nonlinear aspects better than algorithms can. That somewhat randomness when you push hardware and get that break is just really difficult to perfect with code alone, at least to my knowledge. I’m in no way an expert when it comes to dsp coding and couldn’t write anything worth while in it.

    Yeah, the GuitarML, from the background stuff I have read, is doing something similar to an impulse response. It involves using a before and after and comparing the two and learning from the nonlinear behavior the after file. Not quite an impulse though cos it’s much longer than an impulse file, I usually use about five minutes of audio run clean and then run through the hardware setting I want, then let the machine learning to its thing and then tweak things here and there until my readouts are as close to full accuracy

    Thank you :) intrigued by all this

    Oh wait so are the results adjustable? I’m really stretching my understanding here but from what you’re describing it sounds like you basically take two instances, then the machine learning works out from them how to navigate between them … is that to give you something adjustable like the actual machine itself you’re sampling

  • @wingwizard most adjustments I end up making are to the audio files themselves, like the file I ran through the hardware may need a little bit of eq somewhere to adjust the results. There are also parameters in the code itself that you can play with, I’ve been messing around a bit with that just to introduce errors on purpose and see if something interesting comes out as a result.

    I haven’t tried any yet because they would take so long for the machine learning, but there are actually knob captures that you can do as well. So if you wanted to capture a gain knob on the preamp, you would run that clean audio through 5 times, with the knob at 0, 25, 50, 75, and then 100, then it can use those five audio files to create a knob capture. If you figure out the linear signal path of a piece of hardware, you could technically recreate the entire thing modularly, with all knob captures. I may try this soonish on a more simple piece of hardware without too many knobs. More complex ones would still be possible cos you can do parallel paths and whatnot in BYOD, but each GuitarML module is pretty hefty on the CPU, so more parameters would be quite the load. It does capture compression pretty decently too and even EQ bands, but so far I’ve mainly focused on just the saturation. I do wish it could capture time based effects though, that’d be fun to get some old reverbs and delays too but impulse responses still kick ass a lot for reverbs at least

  • A downside of profilers like GuitarML versus a well done non-profiling effect is that most captures are capturing a static setup…all the knobs and switches at one setting. Some captures the dynamics of a single knob.

    A well done emulation may not be as exact at a particular setting but can achieve a wider range of possibilities…and when well done will be quite close.

    I think calling non-modeled techniques “algorithmic” is somewhat misleading/dismissive. A lot of BYOD’s components and many other vintage emulations simulate the circuits in a deep way.

    Capture technology had its limitations, too.

  • @espiegel123 said:
    A downside of profilers like GuitarML versus a well done non-profiling effect is that most captures are capturing a static setup…all the knobs and switches at one setting. Some captures the dynamics of a single knob.

    A well done emulation may not be as exact at a particular setting but can achieve a wider range of possibilities…and when well done will be quite close.

    I think calling non-modeled techniques “algorithmic” is somewhat misleading/dismissive. A lot of BYOD’s components and many other vintage emulations simulate the circuits in a deep way.

    Capture technology had its limitations, too.

    I’m referring only to the GuitarML part of BYOD, and do agree that well programmed plugins aren’t obsolete or anything by any means. They offer a lot more convenience and much less of a hit on your system. But like how hardware has even just the slightest bit of edge over the plugin due to the nonlinearities, I think that the GuitarML profiles capture that magic better. That’s why I haven’t really focused on any other aspect of capturing that part of the hardware, I still think plugins are more useful for other duties. But for that bit of oomph that you get from using hardware over the plugin, I firmly stand by it that the GuitarML profiles sound better. I feel I have expressed the limitations of it quite a bit already and stressed the fact that my interests involve the saturation/distortion etc part of it

  • Lately my workflow still uses plugins a lot, just not for color. I get that from GuitarML and then I’m freed up to just use like Fabfilter plugins for eq duties and compression and whatnot. I think that they work well together to get the overall end result that you want with less tone chasing and fumbling through plugins to get the right one

  • @Fingolfinzz : just to clarify, nonlinearities can be programmed into non-capture type emulations, too. All of the better gear simulations model nonlinear-response also.

  • @espiegel123 Yes, sorry if I seem to be implying that they can’t be cos I’m not intending to. I just think the captures get closer to the sound versus algorithms. Saturn is a magnificent example though of some amazing code though, it really gets closer than other plugins I’ve tried. Their tubes and transformers sound great. I’m just very anal and need that extra little 1%. I’d love to see more plugins making use of GuitarML being open source, a hybrid of algorithm and captures would be awesome so you can get that sound plus the ease of using a plugin

  • @Fingolfinzz said:

    @Zerozerozero said:

    @Icoustik said:

    @bangzero said:

    @Icoustik said:
    Do make sure you set BYOD´s (assuming you’re using that) oversampling to 1x, and turn off the sample rate correction filter in the settings on the GuitarML module, for optimal sound 👍

    thanks. knew about 1x os, but the filter was on. what does that do?

    It just muddies the sound and wastes resources.
    Try turning on linear phase in the same menu as well and see if you can hear that in the Neve

    I’m interested in the Analogxai stuff but confused with setup. The dev has said on another forum that all his stuff is at 44.1 so oversampling should be used if using anything else. iOS is fixed at 48 I think…? Is that what the filter part you mention is to do with? Couldn’t see the linear phase mentioned in any menus…
    I know some users on the forum said they couldn’t use it as they lost too much high frequency info due to sample rate issues/oversampling.
    I’m not tech enough to fully understand all this if anyone can explain in layman’s?
    It may be that you aren’t using in iOS…
    Cheers

    I make GuitarML profiles as well, when you are sampling the hardware (that’s on my end, not yours) I have to sample it in 44.1k due to limitations. Once the profile is made though, the user can use that JSON file in 48k. Linear phase is towards the bottom of the oversampling menu. If there is high end loss, it’s due to the sampling process or the hardware, BYOD should only have a drop in the sub range. It’s a known bug but it’s out there. I actually made some tutorials on how to do it on my YouTube page. It’s the first couple of videos if you need a visual on the setup https://youtube.com/@GrittyToolsFromChamber93?feature=shared

    Thanks for your reply. So it’s ok to use 44.1 in 48? I understand very little in this area but some vague googling brings up stuff about aliasing issues. Is it that it’s ok to go up in sample rate but not down? Or the differences between these two rates don’t matter enough to be audible?

  • @Zerozerozero so it’s fine to use at any sample rate but you have to mind your volume. I think that’s the most important part. Idk if you have ever used Acustica plugins but it’s similar, I actually hit GuitarML at about -18db like you would with hardware and then work my way up to the sweet spot using the gain knob on GuitarML. It will definitely introduce aliasing at any sample rate unfortunately if you are pounding the input but if you treat it like hardware, you get happy results. The only time I’ve had noticeable aliasing is when testing with a sine wave at really high volumes. I think it’s more to do with how BYOD handles it though cos apparently if you’re using Genome, it handles all the files a lot better. I really hope Genome comes out for iOS but apparently NAM is soon. When that comes out, I’ll start making 48k ones for NAM cos apparently they have improved their tech too plus I can record in 48k.

  • @Fingolfinzz said:
    @Zerozerozero so it’s fine to use at any sample rate but you have to mind your volume. I think that’s the most important part. Idk if you have ever used Acustica plugins but it’s similar, I actually hit GuitarML at about -18db like you would with hardware and then work my way up to the sweet spot using the gain knob on GuitarML. It will definitely introduce aliasing at any sample rate unfortunately if you are pounding the input but if you treat it like hardware, you get happy results. The only time I’ve had noticeable aliasing is when testing with a sine wave at really high volumes. I think it’s more to do with how BYOD handles it though cos apparently if you’re using Genome, it handles all the files a lot better. I really hope Genome comes out for iOS but apparently NAM is soon. When that comes out, I’ll start making 48k ones for NAM cos apparently they have improved their tech too plus I can record in 48k.

    Thanks again for replying in detail. Sounds like any issues there may be are pretty minor. Last question! Have you come across any that you found particularly useful for drum bus duties? Nothing too extreme. I know it’s all subjective but would appreciate any suggestions. Thanks!

  • @Zerozerozero no problem! I can nerd out on this stuff all day, I’ve been a bit obsessed haha. I’ve really liked using the tape stuff on drum busses and Neve too. Tubes can sound really nice when not overcooked. I really like the Neve stuff though cos it’s not way too in your face but you know it’s there, the weight those transformers add is amazing. The tape machines are cool too but they do compress a bit, but the Studer one especially, gives this nice high end lift and fattens up the kicks well. I need to get an API sampled soon too cos they have such nice boosts without shredding the audio

  • @Fingolfinzz
    Anything that can add weight sounds promising as does the tape recommendation. Will have a look at Neve and Studer.
    Thanks again for taking the time 👍🏻

  • Just to say that I briefly used byod just with the inbuilt stuff yesterday for the first time on some vocals. It sounds much more organic and amazing than anything else on iOS to me.. I don’t know if it’s just my ears playing tricks. I was really impressed - it wasn’t right for the vocal, but just the character. So now excited about the future.

  • @Zerozerozero thanks! Let me know if you have any other questions!

    @wingwizard yeah, the developer is brilliant, he really writes great code and is very generous to have everything open source. I like checking the GitHub of his and admiring the code. I don’t know if you noticed, but on some modules, you can even edit the components so you can experiment with different capacitors and resistors, it’s like I’m playing with SPICE, it’s really fun to explore

  • https://pasttofuturereverbs.gumroad.com/l/lgxdvz?layout=profile

    looks like this is still live, I've been avoiding it as im skint but seems to have a load of these models in for a decent price...

  • @Krupa said:
    https://pasttofuturereverbs.gumroad.com/l/lgxdvz?layout=profile

    looks like this is still live, I've been avoiding it as im skint but seems to have a load of these models in for a decent price...

    Good place to buy, they have awesome stuff.

  • edited April 3

    @Slush said:

    @Krupa said:
    https://pasttofuturereverbs.gumroad.com/l/lgxdvz?layout=profile

    looks like this is still live, I've been avoiding it as im skint but seems to have a load of these models in for a decent price...

    Good place to buy, they have awesome stuff.

    —-edit: sorry irrelevant, I didn’t realise they sold loops and things as well——-

Sign In or Register to comment.