When I want to build an Oscillator with AudioKit there are different ways to go. For example you can create an AKOperation within an AKOperationGenerator like
var osc = AKOperationGenerator { parameters in
returnAKOperation.sawtoothWave(frequency: GeneratorSource.frequency)
)
but you could also create one with
var oscillator = AKOscillator(waveform: AKTable(.sawtooth))
What's the difference and when to choose what? Thnx!
If you just want one oscillator, it makes sense just to use the AKOscillator node, but if you want to do more than one thing dynamically, operations get you a lot flexibility. For instance, in your operation you can create two operation oscillators - one to oscillator the frequency and a low rate (LFO) and the other to actually oscillate the audio rate signal. There a few playgrounds that highlight when to use operations like this one:
http://audiokit.io/playgrounds/Synthesis/FM%20Oscillator%20Operation/
and the others listed in the Operations section of
http://audiokit.io/playgrounds/Synthesis/
Related
I want to create similar sound to theremin using touch coordinate on screen. I'm using y axis as frequency, x axis as amplitude.
Due to my small research I believe I can create it using AKOscillator or AKFMOscillator from AudioKit framework (please let me know if any other oscillator works better in this case). I'm open to other frameworks like built-in AudioToolbox (MIDINoteMessage etc.) if I can create similar sound to theremin.
Here it says theremin has two oscillators. One with fixed-frequency on 260kHz and one is dynamic between 257-260kHz. It superimposes their output (it takes difference of them I guess?). And it outputs between frequency between 0-3 kHz.
When I create sounds using AKFMOscillator with baseFrequency between 257-260 kHz, it sounds high-pitched.
When I try with one oscillator range between 0-3kHz it sounds very robotic. How I can simulate timbre of theremin?
How can I make it sound better? Should I mix two oscillators? I tried mixing with AKMixer but when both oscillators use same frequency and amplitude, it makes no difference.
I tried to mapping to nearest note (auto-tune), I tried limiting the frequency between 3-4 octaves. It sounds better but still not good as theremin.
What should use ( AKOscillator or AKFMOscillator, OscillatorBank), with which parameters (rampDuration, baseFrequency, modulationIndex, amplitude) to simulate more thereminish sound?
Update:
I did some more research and played with Synth One presets. Now, I know I need two oscillators mixed (both set to saw-shape wave). Changing ADSR(envelope) values to specific ranges creates richer sound (this gives the instrumental sound type). And a lfo to create the wavy (or spooky) sound effect. Playing notes (specific frequencies) creates good sounds, if you play every frequency in between note frequencies it doesn't sound good.
I try to implement self play with PPO.
Suppose we have a game with 2 agents. We control one player on each side and get information like observation and reward after each step. As far as I know, you can use the information of the right and left player to generate training data and to optimize the model. But that is only possible for off-policy, isn't it?
Because with on-policy e.g. PPO, you expect that the training data to be generated by the current network version and that is usually not the case during self play?
Thanks!
Exactly, this is also the same reason why you can use experience-replay (Replay BUffers) only for off-policy methods like Q-learning. Using sample steps that were not generated by the current policy violates the mathematical assumptions behind the gradients that are being backpropagated.
In other words I need two separate ADSR envelopes for OSC bank & Filter (cutoff). How I can synchronize this two things by note press?
AMOscillatorBank(amplitude)->lowPassFilter(cutoff)->AudioKit output
When 'amplitude' & 'cutoff' have separated adsr envelopes, and generate & transform sound by chain two output.
I trying some examples in playground, but i only can create AMOscillatorBank(amplitude) with adsr & only pass some lowpassfilter (but not syncronized to note press).
If you want sample accuracy you'll need to put the low pass filter inside the DSP for the bank. We're actually doing that on the AK Synth One project that will be open sourced when it is finished.
I'm in need of figure out a way of changing the phase of a signal. Objective is to generate two signals with one phase changed and observe the patters when combined.
below is the program I'm using so far:
As in the above setting, I need to use the same signal to generate a phase changed signal and later combine the two signals and observe patters.
Can someone help me out on this?
Thanks.
Using the right inlet of the [osc~] object is a valid way to set the phase of an oscillator but it isn't the only or even the most correct way. The right inlet only permits a float at the control level.
A more comprehensive manipulation of phase can be done at the signal level using the [phasor~], [cos~], [wrap~], and [+~] objects. Essentially, you are performing the same function as [osc~] with a technique called a table lookup using [phasor~] and [cos~]. You could read another table with [tabread4~] instead of [cos~] as well.
This technique keeps your oscillators in sync. You can manipulate the phase of your oscillators with other oscillators, table lookups, and still of course floats (so long as the phase value is between 0 and 1, hence the [wrap~] object).
phase modulation at the signal level
Afterwards, like the other examples here, you can add the signals together and write them to corresponding tables or output the signal chain or both.
Here's how you might do the same for a custom table lookup. Of course, you'd replace sometable with your custom table name and num-samp-in-some-table with the number of samples in your table.
signal level phase modulation with custom tables
Hope it helps!
To change the phase of an oscillator, use the right-hand side inlet.
Quoting Johannes Kreidler's Programming Electronic Music in Pd:
3.1.2.1.3 Phase
In Pd, you can also set membrane position for a sound wave where it should begin (or where it should jump to). This is called the phase of a wave. You can set the phase in Pd in the right inlet of the "osc~" object with numbers between 0 and 1:
A wave's entire period is encompassed by the range from 0 to 1. However, it is often spoken of in terms of degrees, where the entire period has 360 degrees. One speaks, for example, of a "90 degree phase shift". In Pd, the input for the phase would be 0.25.
So for instance, if you want to observe how two signals can become mute due to destructive interference, you can try something like this:
Note that I connected a bang to adjust simultaneously the phases of both signals. This is important, because while you can reset the phase of a signal to any value between 0.0 and 1.0 at any moment, the other oscillator won't be reset and therefore the results will be quite random (you never know at which phase value the other signal will be at!). So resetting both does the trick.
I am trying to understand Q-Learning,
My current algorithm operates as follows:
1. A lookup table is maintained that maps a state to information about its immediate reward and utility for each action available.
2. At each state, check to see if it is contained in the lookup table and initialise it if not (With a default utility of 0).
3. Choose an action to take with a probability of:
(*ϵ* = 0>ϵ>1 - probability of taking a random action)
1-ϵ = Choosing the state-action pair with the highest utility.
ϵ = Choosing a random move.
ϵ decreases over time.
4. Update the current state's utility based on:
Q(st, at) += a[rt+1, + d.max(Q(st+1, a)) - Q(st,at)]
I am currently playing my agent against a simple heuristic player, who always takes the move that will give it the best immediate reward.
The results - The results are very poor, even after a couple hundred games, the Q-Learning agent is losing a lot more than it is winning. Furthermore, the change in win-rate is almost non-existent, especially after reaching a couple hundred games.
Am I missing something? I have implemented a couple agents:
(Rote-Learning, TD(0), TD(Lambda), Q-Learning)
But they all seem to be yielding similar, disappointing, results.
There are on the order of 10²⁰ different states in checkers, and you need to play a whole game for every update, so it will be a very, very long time until you get meaningful action values this way. Generally, you'd want a simplified state representation, like a neural network, to solve this kind of problem using reinforcement learning.
Also, a couple of caveats:
Ideally, you should update 1 value per game, because the moves in a single game are highly correlated.
You should initialize action values to small random values to avoid large policy changes from small Q updates.