I think we can do things with polybuffer~ here, unless I misunderstand the problem.
Understood.
This sounds like a framelib job. It certainly doesn’t sound like a message-domain job, if you are concerned with that degree of temporal precision. Overdrive or not…
I’m probably missing something, but I don’t see these problems being any more difficult in the signal domain than the message domain.
I thought fluid. didn’t play nice with polybuffer~s after the alpha02? Or did I misunderstand that.
Again, I could be misunderstanding this, but if I use @fft settings 512 64 in something like fluid.loudness~, I would be restricted to a temporal accuracy of my hopsize (in this case 64), so if I’m interested in analyzing example 512 samples, I will potentially have +/- 64 samples on either end of it as well.
Whereas with the buf counterpart, my temporal accuracy is 1sample, and I can analyze exactly the 512 samples (or whatever) I want.
Unless what your theorizing would mean that fluid.onsetslice~'s “fft clock” (is that a thing?) would only ‘fire’ when it recelives a click~ at it’s input, and then it would analyze the last n amount of samples with the given @fftsettings.
Plus, if there are diff fft settings (and potentially hopsizes) between fluid.loudness~ and fluid.pitch things get messy again in trying to resync that data together.
But yes, it one/could be trading one set of problems for another.
I think you might, but this is my fault 
tl;dr The difference from alpha 2 was that fluid objects don’t know about polybuffer~s, they only know about buffer~s, but this has no bearing on whether you can use polybuffer~ in your Max code to manage large / dynamic quantities of buffer~s that you pass into fluid processes.
Back in the olden days, I had thought it would be neat if people could pass into a fluid process either the name of a buffer~ or the name of a polybuffer~ (a collection of buffer~s) so that, for instance, all your NMF components end up in separate buffer~s automagically. However, internally, the downsides far outweighed the benefits because the objects are so different. In particular, it proved beyond me to get stable behaviour from objects that needed to access data in the audio thread with this scheme, so I axed it.
All that means, though, is that fluid objects, like all other Max objects that deal with buffers~ only understand buffer~ objects, it has no effect on whether people use polybuffer~ in their patching. As far as fluid.x is concerned there is no difference between a ‘standalone’ buffer~, mybuf and one inside a polybuffer~, say mypolybuf.1.
So, for these things where one needs to throw lots of buffers around, if it’s easier to programatically control the creation of buffers~, go ahead and use polybuffer~
Oh right. So if I set a (single buffer~ inside a) polybuffer~ as output from a fluid. object (say @features mybuffer.1) that would work correctly?
And similarly for source/destination buffers from bufcompose~?
So effectively you could read/write from polybuffer~s, but not address it as an aggregate?
On that note, will something like bufcompose~ automatically populate/size a .1 single buffer~ inside a polybuffer~? Or would it involve “manually” creating the .1 buffer and sizing it and then bufcompose~-ing into it?
Well, fluid.loudness~ doesn’t have fft settings, but your general point remains that your effective sampling rate with a feature extractor is a function of the hop size, yes. However, the temporal accuracy between the signal and buffer versions is no different: the difference is in how much control one has over submitting the portion that gets analysed (which is pretty much what you said, I know).
The equivalent issue in the signal domain is where time-zero is, and how that lines up with the beginning of a sample block of interest. As it stands, time-zero is when you turn DSP on, and we have no further say in how moments of interest line up with analysis windows. I was imagining that click inputs could be used to essentially reset time-zero, but have given absolutely no thought at all to how difficult / disruptive this would be.
But let’s imagine it’s a thing: you have some Rod-grade onset detector, and this emits spikes on events, these spikes could then be used to align the ‘re-starts’ of a bunch of feature extractors, and to some kind of summary object (e.g. a signal rate stats that doesn’t currently exist). At the point that this produces output, you might be interested either in message or signal domain output depending on want you want to happen next. If it’s some kind of query, probably a message.
In my mind, at least, this seems pretty straightforward in terms of alignment and causality, but maybe I’m missing something.
So, totally ignoring actual fluid stuff for the moment, if I were imaging a featurer-y thing in the signal domain, where I really cared about timing, it might look like this:
Obviously, like all MSP things, this scales poorly with the number of features, though mc.* can probably alleviate that from 8 onwards, or framelib, which was was quite literally made for this sort of game.
Yes!
Yes!
Yes!
Should be exactly the same as using any other buffer~, i.e. bufcompose~ will resize, populate etc. polybuffer~ exerts minimal ownership over its contents, beyond naming and offering controls for adding etc.
That’s great!
There should be stuff in the helpfiles for this, as that (potentially) solves many of the n amount of buffer~s problems, and after alpha02 my understanding was that polybuffer~s were a no-go zone.
Might be useful for @jamesbradbury’s problem with scalability in the thread about analyzing slices.
Yeah that makes sense, and doing this pseudo-realtime/JIT stuff is just something I nabbed from @tremblap as a way to being more precise with analysis frames/windows for realtime purposes. But it is actually a real-time process that I’m after.
Something like your (“deep fake”!) screenshot would work well, but is pretty different from a conceptual standpoint to how things presently work in the fluid.-verse.
And if there’s a click~ triggering it, it could (hopefully) theoretically not be limited to a hop’s worth of resolution and also account for different @fftsettings per descriptor type.
I remember ages ago asking for something similar for fluid.nmfmatch~ where you could be very precise about returning a single frame of analysis based on an onset.
There would be tons of uses for this “real-time-but-only-when-you-ask-for-it” type workflow, which would alleviate all the potential issues/problems with the buf stuff.
Totes, that would be great. And obviously makes lots more sense for real-time stuff.
this is funny.
Not really. You are after a discrete time - SC has demand-rate concept, which is what you are after. RT is a stream concept, and Demand is accurately whenever you want, as fast as possible. It is actually nearer NRT for me…
TLDR for all the posts above, but some quick thoughts.
1 - the bottom line here is that the issue is deferring a time critical process - that Max deals badly with threads in terms of user ciliary is pretty obvious from the amount of confusion here, but the reality is most max users thing if a metro is trigging something (or snapshot~ etc.) then it happens straightaway, with no delay. Suffering the defer for something expected to take a long time is fine, but for something fast it is not.
2 - the deferring is all about resizing buffers/nothing else, but it is brought about here by the use of storing buffers as an intermediary - the real time objects don’t/won’t have this issue.
3 - from an engineering perspective the infrastructure in the client layer doesn’t care what the buffer it is writing to is (it’s an abstraction of a buffer that could write to any kind of structure you like), and it might in fact represent anything, so the design implications of being able to output to something other than a buffer would remain at the wrapper/environment level. That is to say it is technically possible to get all the buffer analysis objects to output directly to some other format in max without having to touch anything within the core code. Whether that is a design that might be considered is another matter.
4 - the idea that turning overdrive off will provide an answer is for me quite problematic - I don’t think it is generally viable on a retina screen - see next point…
5 - yes graphics might get put at the back of the queue, but even if they do then once they start processing you have to wait for them to finish to get the next thing to happen. That is why the times are erratic without the defer low (which is giving you the operational time). You’ll get noticeably better performance by opening Max in low resolution mode, but the timing for events on the main thread when hopping between threads is basically an unknown - bear in mind also that anything you trigger off that is still low priority, or you have to up the priority again - not nice. It’s sort of OK for response to triggering general events with a low rhythmic tightness, but it won’t cut it for musical timings.
Thanks Alex,
Yes. Somewhere in the tl;dr is acknowledgement that this mode of use is not (yet/ever) accounted for in the design of the non-real-time objects
Somewhere further in the tl;dr is me wondering if it would make sense to consider how the real-time objects could be augmented to better support the specific type of thing that Rod is attempting, but in the signal domain. You might have good reasons for thinking this is a naff idea though.
True, but there are cans of worms (how is the mode specified? different input and output container types possible? how do we handle intrinsically one dimensional containers (i.e lists)? how could this facility replicate across different hosts?)
/ 5. Thanks for the info. My only response is to shrug and say depends on the music and depends on the patch. I have a piece that currently runs without overdrive (otherwise it deadlocks), and that is fine for those particular musical needs. I think I was clear enough that it wasn’t without temporal consequence, somewhere in the tl;dr.
1 - sure
2 - the issue there is that chunks of audio at specific times looks quite like a whole new infrastructure (a la frame lib).
3 - yes - I’m not arguing it would be easy.
4/5 - For me I don’t think that overdrive off answers either the specific set of concerns here or a more generally applicable set of concerns. Of course there may be situations where overdrive off might be viable, but I’d consider them few and far between. I believe the situation with timing on Max7/8 to be far worse with respect to the main thread than older versions (e.g. Max 4) as the graphics are considerably more involved and also the retina resolution comes at significant cost. If rod removed various graphics from his patches the times would most likely improve significantly, but I think that having to consider what is on the screen in terms of speed is considerably more burden than is ideal. Scrolling around a window with a metro into a click~ and overdrive off on a retina screen should give some indication of how bad it can get. Resizing the window can stop the metro indefinitely. For me that is far too flimsy a scenario to be reliable for realtime usage.
Ok, finally got around to testing this with the latest alpha and it seems about the same.
Sans defer, the results are around 100x slower:
With the defer in place, it is “faster” (back in the 0.5ms range), but the gains here are artificial (rather measuring only the intrinsic and not operational latency).
I may mess around with using the fluid.descriptors~ for the database creation, and then (not.fluid.)descriptors~ for the “real-time” stuff as an experiment since in my intended use case I’m not really going for perfect resynthesis, but rather corpus browsing, so consistency between algorithms (although not great) isn’t paramount.
I’m sure that once you have a working prototype, there are way to spy on the actual read operational load of each solution, with something from developper tools. At the moment, if I was you, I’d use all fluid* approach and focus on quality, but you might prefer quick and dirty. One thing for sure, I’d like to hear the difference both in lag and in sound…
I’ll put something together with rudimentary matching (the simple/core “four descriptors”) and see how that sounds and post the results.
Ok, here is an early test/results. At the moment I’m only matching on 3 descriptors (loudness/centroid/sfm) since pitch isn’t very accurate/consistent (more on this below).
The patch is messy, and the rest of it is still pretty big so I’ll post some patch once I tidy it up, but here is the relevant bit of patch, and I’ve thrown a red panel and labelled the important section:
The audio file I’m playing is about 40s long, and I left it going for about 5minutes just to be thorough on the min/max ranges. The timing is between when an onset is detected and a result is returned from entrymatcher, so this includes entrymatcher latency, which is nominal since the database only contains 3094 entries (vs 100k+).
You can see that it’s pretty fast on occasion, but there are spikes of up to 500ms latency in the mix, with a pretty high average spike as well.
Sonically it’s kind of hard to tell on my laptop speakers since the audio files I’m playing are flurries of notes anyways, so I don’t really “hear” the latency, but this test wasn’t designed to do that (though I’m certain I would hear that 500ms late example…).
///////////////////////////////////////////////////////////////////////////////////////////////////////////////
Part of what I want to test/compare as well is the “accuracy” of the fluid.descriptors~ objects, but that becomes tricky here since my corpus and JIT analyses are looking at very different things.
The corpus is made up of robust stats across the length of entire samples, including statistics based on length (duration, time centroid, amount of onsets, etc…), whereas the JIT stuff is looking at 512 samples and a limited number of descriptors and corresponding stats. So accuracy is more like “accuracy”, at best. A perfect apples to orange-shaped-apples comparison.
This is inherent in this approach though, as I want to analyze audio now and have results even now-er, that there will be a tradeoff. Hopefully in the future (toolbox2) I will be able to stitch together bits of audio ala multiconvolve~ so I can get fast and responsive analysis and matching, but there will always some kind of projection required if I want to trigger longer samples from shorter inputs. (on that note I started experimenting with creating a time series of descriptor data on a microscale, which I would then stretch out to apply to longer-than-the-original target samples (i.e. the pitch contour of a tom mapped to a long string gliss)).
From what I can tell (listening wise), it is consistent though, which is important. The opening onset from my audio clip always triggers the same sample in the corpus. I don’t know if this is the case throughout, and perhaps it’s worth testing to see if I get the same exact results each time the 41s sample plays through.
Here’s the relevant/main bit of the patch for testing. This uses @a.harker’s most recent entrymatcher, but it probably works just the same with older/other versions of it.
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-----------end_max5_patcher-----------
You also need this text file to load into the coll, then dump it into entrymatcher.
analysis.txt.zip (329.0 KB)
thanks for this. I’ll give it a go as soon as Beta00 is out.
Here is some further testing, under different circumstances.
This is playing the 40s sample just once:
So it looks like the 50ms spikes are “real” even under a short period of time.
Here it is again being left running for around 5min (doing nothing else on the computer or Max):
This is what happens if I browse Safari in the background (since I thought this might impact the scheduler):
Pretty big jump there. So much for checking emails while performing then…
I then decided to see what happens if I push Max harder, so I opened the helpfile for hirt.convolutionreverb~ and saw this results after opening it:
And then I reset the trough and peak and got a few instances of hirt.convolutionreverb~ playing to get the CPU up to around 40%. That gives me this:
My takeaway from that is that the performance is super variable in terms of what you are doing. Shit like opening massive helpfiles and fucking around in Safari aren’t exactly good “realworld” tests, but the high CPU load is.
I can’t imagine much has changed on this in beta00, but I did some more testing including a couple more variables.
First was putting a really wide open query in entrymatcher~ just in case some of outliers were a combination of the onset detection working, but entrymatcher~ not finding a result therefore creating an artificially long measurement.
I only left it running for about a minute or so:
So still some slow/high values.
And then to rule out possible funny business with entrymatcher~ altogether, I removed it and just measured the latency for the fluid.descriptors~ part of the patch:
So pretty slow, and now actually measuring the peak times, it’s clear that there are some really long spikes inherent in the process itself. (earlier tests didn’t measure the peak and trough, only the average)
Would it not be better to use a zl stream if you’re measuring the latency over some period of time? A moving window would be much better to smooth than blocks of 10.
