Been trying to set up pseudo round robin on drums using LFO (random, hold mode) → Slice Select, and also tried with Sample Slot. Same problem both ways: the outer slices/slots get triggered way more than the ones in the middle. Tested with 3 slices and got roughly 50% / 5% / 45% distribution after hundreds of hits. Tried with more slices, same pattern — extremes dominate.
Also tried with a ramp/sawtooth at high speed (free running) thinking it would distribute more evenly, but same result.
Has anyone actually gotten a reasonably even distribution across a small number of slices or slots? Is there a known depth/base value combination that works, or is this just a fundamental limitation of how the LFO maps to discrete values?
Not looking for workarounds like conditional trigs or multiple tracks — specifically trying to understand if there’s a way to make LFO modulation of Slice Select/sample slot behave more uniformly. Thanks a lot!
So if I’m understanding correctly, the RNDM LFO doesn’t actually generate uniform random values across the full range — it has its own distribution pattern with some values appearing more frequently, and 0 seems to get special treatment. That would explain exactly what I’m hearing with slices.
Did you find any setting or combination that gets closer to uniform distribution across a small number of destinations? Or is this basically a known limitation of the RNDM waveform that there’s no clean workaround for?
For what you are describing I would utilize the SIN waveform instead of RNDM, keeping in mind how the LFO depth relates to the corresponding range on sample slot or slice select destinations. I recommend using Overbridge to get visual representation on the position of the phase playhead, and also to see what your changes to said parameters are doing. For my above test I polled the parameter values over MIDI Monitor.
Wouldn’t SIN also spend more time at the extremes ? I would have suggested either triangle or sawtooth for even spread, but the OP said he tried sawtooth already.
On the contrary … what you describe sounds more to me like you have too much depth on the modulation connection between where you are and where you want to reach (as extremities)
The depth of modulation needs to be able to span from a zero position to a max position of a parameter - we’re typically thinking in terms of 128 steps to achieve this (in particular on monopolar destinations or with monopolar sources)
If you are using random as a source then you are choosing a bipolar form so your starting point for modulation should be half way along your slice line - then the depth you apply should be proportional to touch the other extremities - most likely at 64 - the easiest test is to adopt a square lfo for setting the depth range - it only does max and min - so set the depth until the lfo is only finding slices at the start and end (round it to the nearest 2^n number if it’s almost at 64, set 64)
Then see how the distribution feels - it should be random, it won’t follow a pattern, it might not numerically be equivalent to a white noise ‘source’ that the random value is being derived from, but i’d expect it to be trend free if set up correctly
The smaller the number of destinations the more carefully i’d suggest you set the depth … if you had four destinations and you set the depth so it just touched 1 and 4 with a square, it’s obviously going to statistically land more often on 2 and 3 when you revert to a random source
If you over-strengthen the depth … it will frequently just ‘clip’ the depth and it will give more values at min and max - setting depth half way is likely the first starting point to try based on how Elektron often configure depth ‘weights’ for certain parameters
No true random will push out every possible value within a range of possible outcomes before ‘repeating’ itself … you can artificially construct something to work like this, but there’s no way the Elektrons operate (or should operate) like that … remember the lfo depth is capable of high resolution, that’s thousands of values, not 10s … they won’t be logging stuff like this
The only way i can think somebody could verify how random it is would be to use a MIDI track (randomly target a cc value) which would produce digital info that could be parsed in something like Max … let it run long enough and a distribution should become clear
there was no way i was going to do this for the full set of values, thankfully it’s fairly easy to work with digital numbers in a binary way
so the MIDI CC value is monitored in Max and its ‘default’ CC value is set to 64 and a (half) modulation depth of 64 is used to target the CC value using a random lfo
this just pumps out endless CC data at a fast rate and i count the occurrences of ‘buckets’ of value ranges … so for 0-127 a 7bit range it’s easy to bit shift the incoming number to get a 4 bit range (0-15) which mirrors exactly what happens if you have a reduced range of target positions (or slices as above)
in the real data if it’s 0-7 it equates to 0 , 8-15 equates to 1 etc - so it’s still statistically valid (and quicker to test and easier to visualize)
These target numbers are counted and when any target count gets to 128 (arbitrary, it could have been 100 or 150, obviously more is better) it logs the tally and clears the count and starts again …
I ran this 8 times just so you can see how consistent the distribution is … there is no biasing … i’m actually surprised at how evenly it is distributed after a relatively short run (circa 1600 CC values per plot)
I think if you use the correct range, the correct starting point you can be very confident the results are going to be truly random
things obviously get more complicated when you have fewer and fewer target values because the selection of a ‘mid’ point becomes tricky to attain
what’s the default midpoint of 0-127 … Elektron assume 64
if you have 0-3 either 1 or 2 are equally close to the true centre (an un-selectable place between 1 and 2) but if you specify a starting point of either you bias the likelihood of bipolar values landing on that side of the centre – you can mitigate it a bit by dialling back the depth so the incursion into the clipping side is minimal
Anyway - the point is more to say that in real-world use on these devices i’m entirely satisfied that random is random, there’s no inherent biasing on show, you just have to use it well
here’s the 8 plots - each column height is a count, the highest column in each plot has reached 128 … the plots show the counts for target values 0 through 15
great empirical test. what’s happening under the hood is a quantization issue - you’ve got a continuous random LFO value getting mapped down to a small number of discrete slice positions. when the mapping isn’t perfectly centered and the depth slightly overshoots, the floor and ceiling values absorb all the overflow, which is exactly what produces that 50/5/45 distribution the OP was seeing.
avantronica’s fix is the right one. once the depth is calibrated so the LFO range maps cleanly across the available slots without clipping, the distribution flattens out. the plots confirm it.
please note that it’s not advisable to have a fast rate which is also coupled to the tempo otherwise you can get bit reduced versions of these shapes and you won’t see the same trends because it’s sampling points are too infrequent relative to the speed
I think the typical guidance is to set the default slice to the middle value (or as close as you can get). Then the depth of the bipolar LFO needs to be limited to match the range you need to cover.
For example, if you were doing three slices you would set the default slice to 2. Then the LFO depth would be set to go up 1 (to 3) and down 1 (to 1). I think that depth is 1, but someone might correct me.
yeah, good point - like some other parameters - they have bespoke mapping on slices that’s very context sensitive … i don’t much align with this, but in terms of depth you can set a depth and it will do as you describe … it’s linearly mapping depth to slices
numbers are no longer about percentages of a full range, they are explicit - depths should not exceed the slice count (or half that)
It maybe makes sense in some cases, i’m not sure this is how i would like it, but it’s how it is and i can reluctantly see some logic
so depth (and starting slice) are ideally about half the ‘current’ slice count if you want to land randomly on any of them
so extremely easy to clip slice selection with small total slice counts !!
Yeah OK, this is incorrect. In the entropy thread, my example was also born out of a parameter where I wanted randomization but I did not want the base value of the parameter resting at 64.
But also true - context dependent. For the record I am assuming in the context of a 0-127 value parameter. Assuming 128 slices, like you say, first things first, the base starting point should be in the middle (64). Second, like was also said, faster speed and/or multiplier will equal more skips.
A sine wave @ CC VAL1 (0-127) BASE VAL 64 / MULT 1 / LFO DEP 64.00) will hit at least 124 out of 128 possible values on first pass in this context (approximately 5 seconds)
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A random wave under the same config in my testing hit 98 out of 128 possible values running for 1 minute. For 10 minutes all 128. That being said:
1.) Again, project and context dependent, it is about choosing the right tool for the job based on the other factors of the project. Sample order, trig positions, tempo, etc. Obviously I wouldn’t use the sine wave as a random generator under that exact config above, but suggested as a baseline for further modification as you can see more guarentee to hit more unique values in a shorter amount of time. Triangle wave should be identical, and a saw will obviously do the same in half the time.
2.) If you map the random wave values to a graph there are patterns in how the values jump. For something like 128 grid slices on a drum loop, In my experience this is audible to me more often than not based on the groupings (disclaimer: this obviously depends on the loop). In the context of some synth parameters, equally so when dealing with exponential curves.
