Descriptors comparison (oldschool vs newschool)

[tremblap]

Is it even possible though? That’s what I’m asking.

yes it is. like in jitter, you have to decide who is the boss. When we go public with the multithread implementation I’ll put examples in there.

Or am I mistaken here?

tremblap:

to a certain extent. FrameLib allows to do frame accurate at (multi) audio rate. What you do is ‘slow’ in computer terms, and easy to sync when disciplined. I’ll do examples. The main hurdle here is that you still think in parallel. Computers are mostly serial machines, so you could recode your example and take the chain of events in total control.

It’s not a good comparison because what makes an EQ or other plugin better is a better algorithm which costs more.

and interface. FFTYin is more expensive, but better, than the pitch in descriptor~ For info, ircam.descriptor has it too, and is more expensive than Alexs, but for many other reasons too. Implementation, interface, quality, are all intertwined.

For instance, forking in code has a cost. Every option you give to a user implies forking in the code somewhere, at best. Architecture is everything in code optimisation. For instance, asking for different ffts in alex’s would raise the cost significantly, since one thing that makes it efficient is Alex’s clever order of manipulations to compute as less as possible. This comes at the cost of flexibility. That is why he spent so many years on FrameLib

I see the benefit of having control over separate fft setttings and parameters, but the cost (as it stands) appears to be too great, particularly if the choices are between doing JIT and being slow/expensive or doing realtime and being inaccurate (frame synchronization).

Then use something else if the cost is not worth it. it’s that simple. But first, try to compare for real, i.e. how better you describe (and more importantly, match) when you have what you want for real. Then, like plugins, pick your best cost-to-feature ratio.

I will try making a more comprehensive comparison patch, and will include the resulting outputs too to compare it in terms of “real world” usage, that compares diff bufcompose -ing order, and peek~ instead in places.

if you do that you need:

1. description
2. matching
3. matching with what you gain with the 2nd interface, which should yield better results

then you can compare implementation cost vs results on original task and aims, not on using a ferrari to plough a field… which is an analogy used in other threads. It is an important one: there is no free ride, not object/architecture that is better, only some better for some tasks. Here we try to help you find the knowledge to actually compare that -what you think you need to do a task you think you want, then by having access to the tools and knowledge to explore all your dream implementations, finding which one is best for the task at hand.

In other words: in some of my composition patches, I will still use descriptors~ (when it is corrected ) when it is the best for the job at hands. What I learnt through the fluid* version (and the python prototypes, and the audioguide software) is that what I thought was better is at times worse… and at times has better results, at higher cost.

[rodrigo.constanzo]

yes it is. like in jitter, you have to decide who is the boss. When we go public with the multithread implementation I’ll put examples in there.

So are you saying that something like this would all correspond to the same frame of time?

I thought the right-to-left thing only applied if you stayed in Max land, but since each of these objects was getting audio, and their outputs wouldn’t correspond to right-to-left order, or a known order since it would be different each time you loaded the patch(?).

For instance, forking in code has a cost. Every option you give to a user implies forking in the code somewhere, at best. Architecture is everything in code optimisation. For instance, asking for different ffts in alex’s would raise the cost significantly, since one thing that makes it efficient is Alex’s clever order of manipulations to compute as less as possible. This comes at the cost of flexibility. That is why he spent so many years on FrameLib

Yeah totally, but I’m saying that the present method/architecture has an overhead that has nothing to with what you’re discussing. Going in and out of Max that much has a baseline cost beyond what’s going on in the object(s). I’m all for fancy algorithms that cost more, but this isn’t that.

I’ve started coding another comparison. I’ll post it once I get the right values out of (it’s not producing the same results for some reason), but I’m slimming it up by pre-bufcompose~-ing only the “rows” of descriptors that I want before bufstats~-ing them.

So far, this is actually slower than the previous version I posted (though going to the same fft settings for pitch speeds it up some).

As I said, I’ll post it once the numbers are correct, but the amount of channels and such being processed is correct.

I wonder if having an extra bufcompose~ step is responsible for the loss in speed? (so perhaps analyzing “all” the channels is cheaper than separating the channels you want to analyze first)

[tremblap]

So are you saying that something like this would all correspond to the same frame of time?

definitely not, as you are streaming, which is not what you do in your original. I would go through a buddy object, as this is one way Max-land has tried to help their idiosyncratic timings… but I would do like you do elsewhere (inspired by my JIT buf processes) and process a given windowed buffer.

Yeah totally, but I’m saying that the present method/architecture has an overhead that has nothing to with what you’re discussing

I know that is what you say, but I tell you I disagree. It has everything to do with what I’m saying. Flexibility and power comes at a cost. Your desire of a good pitch (fft param A with algo) amplitude/loudness (other settings) and spectral shapes (another setting) with the bias of window size you want and nervousness and post attack, all 3 having different consequences on what works well, would make the blackbox approach impossible apart if it is coded for that specific case. So you need flexibility. So you need the current architecture to get it. So it has 100% to do with architecture, because, as I said many posts ago, these are all intertwined.

[rodrigo.constanzo]

definitely not, as you are streaming, which is not what you do in your original. I would go through a buddy object, as this is one way Max-land has tried to help their idiosyncratic timings… but I would do like you do elsewhere (inspired by my JIT buf processes) and process a given windowed buffer.

Yeah totally, what I’m doing (for now) is better in a JIT context, but I was pointing out another (potentially big) problem with the current architecture in that if you wanted to analyze in incoming audio stream for more than one type of descriptor, you can’t synchronize that data (as in my screenshot).

It’s another place where this architecture is working against the ideas you’re putting forward (flexible, powerful, etc…). If you can only do realtime analysis of one desriptor type at a time, I wouldn’t say that’s very powerful.

I’m actually quite curious what you guys are/were thinking for doing matching in the future if it’s not possible to synchronize realtime objects (would all analysis/matching be JIT?)

[groma]

I have not read all of the thread, but I will comment on some of the issues.

First, the descriptor computations are interrelated. In order to compute the spectral spread you need the centroid, for the skewness you need the spread, etc. So you will be computing them anyway depending on what you request. The algorithm for pitch and confidence is also the same, there is not a specifically added cost of computing the confidence.

About the interface, I think an important point is many people, even people that have a good understanding of the DSP, do not necessarily have an understanding of the musical utility of descriptors. By grouping descriptors coneptually I hope we are helping to make them more easy to understand and use as well. This interface is also easier to port across platforms other than Max.

I get the point about RT synchronization, and I personally don’t know enough about Max scheduling to understand how it works in detail, but clearly with sufficient overlap the evolution is going to be smooth, and the descriptors will correspond to approximately the same point in time. With this configuration you can also use different STFT parameters for different things. For instance pitch may require a larger window, but other descriptors you may need for rhythmic events you may want to use shorter windows and less overlap, which I think is also interesting.
We have not considered “matching” too much as a use case for the first toolbox, as it woud be more of a topic of the second toolbox

[rodrigo.constanzo]

I totally get how splitting them up into categories makes sense, though I don’t know that the general level pitch of the project (“being able to code a granular synth from scratch”) would need too much help with descriptors. When you’re dealing with stuff like nmfs, audio descriptors seems like the least of one’s worries!

It is also possible that one can use multiple fluid.descriptors~ objects at the same time, going to separate parts of a patch, so synchronicity isn’t important.

I only brought up the realtime (and later, matching) use cases because those also seem to be problematic with this architecture, on top of the problems in my specific use case(s).

So at the moment my options are using JIT (with a 10-20x latency increase in computation time) or use the realtime objects and have decorrelated frames of analysis for each descriptor.

[tremblap]

So at the moment my options are using JIT (with a 10-20x latency increase in computation time) or use the realtime objects and have decorrelated frames of analysis for each descriptor.

This sentence is wrong in both cases.

• The computation time in JIT is 6 times, not 10-20, for similar features.

• For RT, you just need, like for any real time thing in max, to be discipline about sync. I recommend buddy which will give you as true as you will get.

Continue your tests, as now, you can do them, and see the cost/benefit analysis in your case. As they way, your mileage may vary.

[a.harker]

A significant part of the cost with the flucoma objects is the pitch analysis (c. 3 quarters - this is a more complex algorithm, although having read the yin paper at the time (when it was under IP restrictions - I don’t remember the complexity being so high).

Another part of the cost will be due to 64bit floating point calculations (rather than 32 bit). Another part will be calculating much more values at various stages than you need each time/calculating multiple FFTs, copying between buffers and so on.

I have already criticised the use of FFT bins as a parameter. If log freq is used these actually won’t be relevant units any more (nor will Hz or normalised frequency). I see no reason to return in these units, and indeed I’d prefer normalised freq (my preference is nyquist at 1.0, not sr at 1.0 but there are in fact two variants of this). Returning in midicents is not relevant to the outputs other than centroid, as they are linear valued output that cannot be meaningfully translated in a log representation. As the sample rate is in all cases known there is no reason not to convert, and indeed, even without the sample rate you always know the normalised frequency. There is absolutely nothing to be gained in returning these in bins.

Finally, the main additional flexibility here is being able to take time series from the objects (a feature that I had planned for a future descriptors version) and you have the power to deal with edge cases as you wish, but I do not believe that to be particularly useful. I am sure we will continue the conversations about relevant interface, particularly in the case of edge cases, but PA seems quite sure of some of the decisions so far, and I have a limited desire to argue out these points, given my investment in this area for many years if that input is not wanted. At present I personally find the solutions to be technically, rather than musically centred in a number of ways that I find frustrating, although the grouping is indeed helpful.

[rodrigo.constanzo]

The computation time in JIT is 6 times, not 10-20, for similar features.

My original test was 1ms vs 0.05ms, the improvement left it at 0.5ms vs 0.05ms, hence the 10-20x speed difference.

For RT, you just need, like for any real time thing in max, to be discipline about sync. I recommend buddy which will give you as true as you will get.

Unless I’m not understanding, I don’t think buddy does what you’re saying.

buddy will spit out once three messages are received, but based on whatever the slowest message is, so it won’t be time aligning/syncing anything.

Do you mean something like this?

Does that work? I didn’t think signal->control worked that way, to allow syncing of data back up.

There is absolutely nothing to be gained in returning these in bins.

Now that I understand what is happening and how to convert the values, I don’t understand the reason for using something like bins in the first place.

[a.harker]

Real-time values from these cannot be relied on to have the same analysis frames exactly in all cases even in the case of the same hop size (the other FFT settings are irrelevant - if the hop sizes are the same then in theory the values can be aligned).

However I think that if the hop sizes are the same, then you probably don’t even need a buddy to get exactly synced frames. The reasons for this are fairly involved, and this assumes that all objects receive their audio input from a single common source. This behaviour, however, cannot be relied upon. As soon as you change fft settings on the fly then all bets are off.

[tremblap]

buddy will spit out once three messages are received, but based on whatever the slowest message is, so it won’t be time aligning/syncing anything.

In fact it will, like in your live example below, as long as your slowest computation is not longer than its period, your certain to catch the last valid value of the other 2. That is, as @a.harker explains, if your fft parameters are of compatible values by design . But for RT descriptions that need to be sync’d I’d either use JIT for time series management, or other options for short, time-critical, efficient methods, like FrameLib or descriptors~

I have already criticised the use of FFT bins as a parameter.

Indeed and that has been taken on board. It might even be actioned soon but that depends on the whole reasoning of all party involved, and often times, I’m explained good reasons

indeed I’d prefer normalised freq

really? That might have been my call, as I thought it was abstract as hell (and 2 variants made it ambiguous). Interesting!

[a.harker]

As mentioned elsewhere I only prefer normalised frequency to FFT bins (not to Hz which would be my preference). I could have been clearer above.

Normalised frequency is common in the DSP literature. I don’t find it abstract at all and it is much more likely that the user knows the sample rate rather than knows what FFT size they set, and how many bins to expect from that etc. But Hz is unambiguous and easy for all to understand.

[rodrigo.constanzo]

Another interim test, with surprising results.

Having no bufcompose~ at all, and just using the frame/chan messages to bufstats~ is even slower for some reason…

Left to right is the vanilla implementation from my first patch (analysis->compose->stats->peek), a version with more curated bufcompose~ where I narrow down the channels I want before stats, then a version with no bufcompose~ at all (analysis->stats->peek), and finally the vanilla descriptors one.

I also tested @algorithm 1 and it is faster, but not by much. It goes down to about 0.3ms average instead of 0.5ms.

[rodrigo.constanzo]

Ok, it took a bit to fix all the numbers and to make a more comprehensive comparison patch (we have friends staying over at the moment), but here is the patch.

The processing time of all the fluid.descriptors~ patches are all pretty slow, but the performance of @a.harker’s descriptors~ is quite erratic in places.

Have a poke through the different sound examples at the top and look at the results at the bottom to see what I mean. (playing the same exact sample of frozen noise is a good example) [is this due to what happens with edge cases if the fft size doesn’t perfectly match the window being analyzed?]

There are a few blue subpatches that you can see the results in different ways with, so have a poke at those. You can also play the frame that’s being analyzed. In order for this to work properly you should stop the record in the main patch to make sure you play the correct bit.

Let me know your thoughts.

edit: updated code to work with alpha07.

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-----------end_max5_patcher-----------

[groma]

At this point I think it is worth mentioning we are focusing on design rather than optimization, which is a common practice in software development, so hopefully there will be further optimizations once we freeze the design. I can’t load your patch as it is missing bufview.js, but if your focus is performance I think I would try to analyze each descriptor object differently, and they are definitely not direcltly comparable to framelib.
I do appreciate your effort playing with the descriptor objects, I don’t see them as newschool with respect to framelib performance-wise, but they do have a different interface.

[rodrigo.constanzo]

(the bufview.js should be part of the FluCoMa package, but either way it’s not integral to the way the patch works)

Yeah, I appreciate that it’s a different paradigm interface, which I don’t find optimal, but it’s not huge problem. I’m just trying to see what the practical cost of the paradigm is as I think there will be a performance hit/limit regardless of optimization since it has to go in and out of Max-land and buffers a whole bunch for each step.

I’m working on a comparison patch between using an even smaller analysis window (256frames vs 512 in the posted patch) to see if that makes up for some of the loss in speed/performance. I have to play with it more though because it is super spike-y in terms of CPU (getting 40ms delays in the middle of everything else).

I don’t know if the deferlow mucks things up for the purposes of timing, but I’ll post what I have as soon as I get it working.

[rodrigo.constanzo]

Hmm, I don’t know if other things have changed in the code in this latest update, but in testing it now with alpha07 and sans derferlow the performance is significantly slower. Like 500x slower…

If someone can test the patch I posted above to compare that would be useful, but this seems pretty crazy.

edit: it looks like removing the deferlow slows things down. Even with the deferlow in, it’s still much slower than before (taking 5ms instead of 0.5ms).

I wonder if using deferlow makes it actually faster or if it just reports it being faster due to how slop is calculated?

[tremblap]

OK I compiled again with all optimisation on, and I get the same results as you. What I cannot get is the quicker version you had when I revert to alpha06! Do you have the code you used to use? Also, I’m sure @weefuzzy will have some wisdom here…

[rodrigo.constanzo]

It’s the same as what was posted before. I just ran it with the plenary version of alpha06.

[weefuzzy]

Also, I’m sure @weefuzzy will have some wisdom here…

Well, first thing is to see if any version gets back to prior performance, so try Alpha 5.

Then, rather than guessing, you can use Instruments to check the heaviest thread with each behaviour, and then we’ll know what the culprit is.