Descriptors comparison (oldschool vs newschool)

I’ve tested that too, but I just wanted to know what the value is for any given clump of readings without seeing the numbers change each reading.

Ah I see, I didn’t realise you were testing something ten times rather than just leaving it running and seeing what the numbers would do.

ok now I want to hear the results - and more importantly comparing to the other patch you used to use.

Can you give us a bit of Shania from both patches (old and fluid*)

Not exactly a bit of “naked Shanaia” but here’s a vid I’ve done with some testing:

From using it in context I have to say I don’t feel a latency or anything, but it could be because of the fast/sputtery material, that any latency just gets felt as missed onsets, which I’m used to feeling.

Again, not tested it in a super scientific way, but the descriptor-side of things doesn’t feel great in that the samples getting matched feel more random than anything else. Mainly because I’m analyzing an entire sample for the corpus, and querying that with only 11.61ms of audio.

I can try to improve this some, but not really sure how, short of just “wait for toolbox2”. I can also just analyze the first bit of the audio from each sample of the corpus and match using only that, but again, not ideal.

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I saw this (amazing) example in your twitter feed, but as it did not mentioned the project, I was not sure what you were using. It is very fun!

I think there you do indeed have a paradox, and won’t be solved easily as it is a temporal problem: how could you know what the future holds?

So I would try the following (which is not far from what I wanted to do when I started the project)

  1. analyse the corpus 3 ways:
    the first 11ms
    the first 50ms
    the first 250ms

and then match the same windows on each attack (after 11 playing 50, after 50 playing 250, after 250 playing the whole sample until another attack)

that is not dissimilar to ‘multiconvole’ and is just 3 grains, but could be a fun route to explore? Just 2 to start with maybe?

The corpus thing is only a small part of the video, not to mention a complicated thing to try to explain in 140characters, but I’ll have more detail/info as things develop obviously.

Exactly. Eventually I’ll do something like what you’ve suggested, ala multiconvolve~, with hopefully better playback/blend/fade/whatever options from toolbox2, but until then I don’t know how effective that would be if I’m trying to query and stitch something together in a sample accurate manner (to avoid discontinuities and such).

I guess another option would be to analyze only the first 100ms of the file, since that at least would be more representative of what I’m feeding it (rather than 8s+ files).

But the problem of time-travel is not really solve-able. I’d be happy with at least something consistent and playable in the meantime.

I don’t know how well that will happen in toolbox2 either! You could just crossfade for now and do a proof of concept.

that is what I propose with different timings: if you did a short mid and long term window you could then play them as such! basic cross fade to start with, and offset in the start, and boom, you have something that is a bit like when Diemo stitched CataRT with MultiConvolve~ and could be a fun prototype to try.

I meant analyzing only 100ms and then querying that with 11ms fullstop. Not stitching. As potentially a better solution than analyzing the entire sample and querying it with a tiny fragment of audio.

Even with stitching, there will be the problem of temporal mapping as I’m largely using sounds that are quite short, and want to have samples that are arbitrarily long. So even with perfect stitching, I wouldn’t want to play a 100ms sound and get a 100ms sound back. (an idea I have for this is to use the time series from fluid.descriptors~ objects and time-stretch them onto longer samples, so something like multiconvolve~ meets a clock that’s slowing down)

I’m going to try building something that analyzes the first 100ms for a few of the descriptors (loudness max, pitch median, centroid mean, flatness mean, rolloff max90, spread mean) as well as analyzing the entire file for all sorts of stuff (and related stats).

I’ll then query and match something based on the first 100ms and then analyze that subset again for the best match within that pool too. That way I can get something that’s relatively close to the 12ms analysis window, but also matches the whole file too.

Perhaps this is more a question for @a.harker, but in all of the C-C-Combine stuff I’ve been using the <-> matcher because I’ve wanted something within a specific window, to use as a pseudo-weighting. In that use case, it is not unusual (and often desirable) to have a query return nothing.

For what I’m doing now, I always want a query returned, even if it’s very distant. I want the best possible match of the choices available. So is the distance matcher a better choice here? How does it work if you chain queries together?

Like in my example, I want to query the first 100ms (separate entries in entrymatcher) for loudness/centroid/flatness (in that order). If I use the distance matcher, will it return the single closest value for loudness, and then the single closest match for centroid of what’s left, etc…? Or does it pick a single match that is closest to all three descriptors? How would that work if I then want to query the returned subset based on that?

I guess I could still use the <-> matcher, but with really wide criteria (loudness_max <-> 100. -106.656967) and just narrow the queries down as I move along, but I guess this can run the risk of not returning a match if I go down the chain far enough.

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My trick is to use a feedback look with the right most outlet.

image

In this example I’m asking for the x/y of a specific example. These could be any data you want it just hapens that mine are neat coordinates in this specific case.

I then take the data which is indexed (x/y coords) and shove it into a match using <-> but with the scope being the maximum positive and negative range of the data set. Because mine is normalised between 0. 1. this is pretty easy and 1 will cover all my bases. You might need to first query the database for the mins/maxs so that you can format your message appropriately. For example, if your min/max of centroid was 3000 and 9000 respectively I would just search for something within 6000 of the test value. Because I’m matching using match 2 I only ever get the two closest matches when I zl slice the results. It’s not super pretty but it works well and is faster than any alternative.

I not so recently made a group of framelib objects (fl.closest~, fl.find~) that can do matching on buffers and returns channels/frames. I did begin making some patches that interacted with data from fluid stuff but I didn’t take it very far or test the implementation much. If thats something that interests you, I can take another look at it and make something that works and could be used

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I’m doing something like this either way, by scaling to the min, mean, and max of the data set. (or rather, I will be implementing that soon, but I do so in C-C-Combine).

So the idea is to the still query a crazy large range, and have it return what the closest thing is. But what does it do in terms of weightings? Like in your example, since x is being queried first, will it find the closest 2 matches to x and then find the closest value to y from that subset?

(say you want x <-> 1 0.3 y <-> 1 0.7, and in the data set there is an entry that has x = 0.35 / y = 0.1 and x = 0.9 / y = 0.65, would it return the first entry or the second?)

I know it chains queries together, which can be very effective for ruling out a lot of the dataset at once (using == for example). But I’m just not clear on what happens further down the set of queries from there if you are using super wide open <-> queries.

Tangentially related, I’m pretty sure I’ve posted this here before, but this takes a minimum, mean, and maximum for an incoming dataset and a corpus and scales them to each other in a smooth/slick way:


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Hmm, firstly the normal quotation around ‘fl.find~’ is killing me, and secondly, how is the speed performance of this in the fl.universe~ vs peek~ and/or fluid.bufcompose~?

The distance is euclidian IIRC, and those two things are equally spaced, so I assume there is some other precedence that determines what is returned. My feeling is that it returns the first index it finds, so whichever entry occupies the lowest number idx will be returned. In that case I would ask for 5 or something and do some extra filtering at the end. No matter what you do you will always have that problem unless you specify another set of criteria to search by.

As you can imagine it is fast. The searching is at audio rate and I haven’t been able to make it crash yet, however, I haven’t profiled or looked into how I can implement the searching in a better way. Like I said its mostly untested and I just wanted to see if it worked out on some small scale experiments. Theoretically you can retrieve a match in one vector of audio…I think…

Ok, I think I get it. If I have a wide open criteria, it will then pass it forward, and do a euclidian distance of the whole dataset, whereas if I do a tight <-> query, it will completely rule out everything outside of that range moving forward (potentially nothing).

I got super excited, but then my excitement was tempered when I realized the slowness is the fluid.descriptors~ paradigm is with the writing of the buffer~s in the first place, not the retrieving them. I guess fl.find~ would be faster than peek~, but it will still be waiting on fluid.descriptors~ to do its job first.

You always get the distances of your matches to the criteria, but if you ask for the whole space you get an indication of how far everything is from a known point.
In regards to speed, this is why you use the framelib descriptors, some sort of buffer (fl.register~ or a buffer~) and fl.find~ or closest. Sorry for poor formatting phone bad computer good.

To be pedantic ( :smiley: ), it’s not the writing but the possibility of having to resize them which means it has to be deferred in Max (and – to be even more pedantic ( :smiley: :smiley: ) – there isn’t slowness in the paradigm per se, but in Max having switch threads when overdrive is on; rest assured your desire for an overdrive-able just-in-time type of thing remains in my mind :hearts:)

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As an aside, I finally did some testing where I’m hearing the pre-analysis audio along side the queried samples and the latency is most definitely perceivable, but also the “jitter” of the latency too. Doesn’t sound too great…

(up to this point I’ve been using purely acoustic sounds to trigger, with no ‘thru’ sound, so the latency gets a bit masked by the physical acoustic sound)

So I’m thinking that in the short term it might be better to just use the vanilla descriptors~ for real-time use, but use the fluid.descriptors~ stuff to create the database. It wouldn’t be apples-to-apples, but since I’m comparing an 11ms onset to a >8s sample, I don’t think it matters too much.

BUT

In doing some more detailed comparisons, I’m getting some funky values from descriptors~. (hoping @a.harker can shed some light on this).

A stripped back test patch posted below, but here are my results for various tests.

First, a 25ms burst of pink noise scaled to 0.01 amplitude:

Pretty good, pretty good.

Here is the is the same length burst but scaled to 2.0 amplitude:

descriptors~ reports, um, a quieter sound…
(to be fair, I do sometimes get a value in the -50dB range for descriptors~, but it varies pretty wildly, and is very close to the 0.01 burst either way)

Next up is a fixed white noise burst (so it’s always the same sample), of either 10ms or 20ms in length (results are comparable).

First at 1.0 amplitude:

Next at 5.0 amplitude:

Again, descriptors~ reports a very small difference in loudness.

Last couple tests are with pitched material. First a 55ms 880Hz sine wave at 0.5 amplitude:

Perceptually I put this in line with a noise burst at 1.0, which is around where fluid.bufloudness~ is reporting it. descriptors~ has this almost as low as the nearly inaudible 0.01 pink noise burst.

Finally 55ms 770Hz saw wave at 0.5 amplitude:

Similar results to the sine wave test, but it’s worth mentioning that running the test over and over again results in a +/- of around 1dB for fluid.bufloudness~ but descriptors~ shows a variance of closer to +/- 4dB.

Here is the code for the test patch:


----------begin_max5_patcher----------
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-----------end_max5_patcher-----------

//////////////////////////////////////////////////////////////////////////////////////////////

I remember there was some discussion about @tutschku’s loudness test at the last plenary, but no details about it specifically. And @tremblap has reported some similar (sounding) issues with descriptors~‘s (descriptors~’?) loudness in the past, so I’m wondering if this is the same problem and/or if this is intrinsic to how descriptors~ works, or if there is a problem in the algorithm somewhere.

quick question: have you used the exact same sample between all tests? Not just within, but between? If yes, I would have expected an exact value difference I do not have here, so I’m worried…

I’ve done both. There are some “live” (random) noise tests, as well as some fixed noise tests in the example. Obviously the random one should be a bit different (though within a reasonable range), but the fixed ones should be the same.

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indeed, and the descriptor~ one is broken and not my issue, but I’m doing my usual QI job for the fluid* side. I was expecting exactly 6.02dB for the double, so I hope that example was random (between 1 and 2)

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