Real time audio buffer synth/Real time image smudge tool
This topic is about investigating ways to achieve real time in Pythonista in situations that are extremely real time dependent.
These situations are generally found in two fields. Audio (synth, filters, sound processing), and Image (smudge tool). What makes these situations difficult is that, as opposed to usual real-time applications, the computer doesn’t just need to have your ui input and a few variables to update the internal state of the program and play/display whatever it should, it instead needs the actual data that is being played/displayed (the screen image or the audio output). It’s a lot more data than a few hidden state variables. So while you‘re hearing the audio/seeing the screen and deciding what to do next, the computer is actually also working directly on the current audio/screen data in order to compute the next thing you will hear/see, the challenge is then to deal with these often large amount of data fast enough to deliver it in a seamless fashion to you.
Here are the advances we made in the audio department (see the end of the post for the image part)
Real time audio processing in Pythonista:
At the lowest level, real time audio processing is achieved with a processing (also called circular) audio buffer (a kind of array). Basically, the code iteratively fills this buffer with the next bit of sound and send it to your ears when you’re finished hearing the current one.
Most audio processing effects (filters, for instance) needs the very data that you’re currently hearing to compute the next bit of sound. So before sending it to your ears, they will copy that data and start working on refilling the buffer with the next part before you’re done with the current one and come back for more sound.
@JonB AudioRenderer wrapper (see below) is a great solution to have access to such an audio buffer functionality in Pythonista.
Here are my current modifications of @JonB files to get an antialiased sawtooth instead of a sine wave, a 4 poles filter you can control, unison, vibrato, chords (with several fingers) and a delay, all in one buffer/render method:
It’s set up to play one chord with number of notes = number of fingers on the screen (up to 4). You can control the filter by moving up and down the first finger having touched the screen.
It’s an inefficient implementation because all the work is asked in perfect real time as if the control parameters were changing on a sample by sample basis. It’s at the edge of glitching (to see it, just set the filterNumbers to 16 and notice the glitches when creating filter sweeps). The solution for that is to compute audio elsewhere with bigger anticipation chunks of audio corresponding to, say, a 60Hz rate (because that’s pretty much the rate at which touch_moved are received anyway) and then progressively fill the circular buffer with the computed data.
Real time image smudge tool in Pythonista:
A real time smudge tool (See also my second post) works similarly to a filter or a reverb, only it processes regions of an image along a brush stroke rather than bits of sound along a sound stream. @JonB ‘s IOSurfaceWrapper (see below, and thanks to him) made it easy for me to code a real time smudging tool (lots of comments in there as well):
It can be interesting to take a look at my previous approach, especially to compare their speeds:
The difference, on my device at least, seems small at first glance, but when you smudge in a very fast circular fashion (around a small circle) you will notice that with my previous approach, the blue cursor can’t keep up and ends up being on the opposite side of your finger’s circular motion, while with the current approach, the cursor is always perfectly in line with your finger.
And regarding the sum of sines, for a 100Hz note you would need to go up to
Nyquist=22050so approximately 220 sines to sum just for one note. If you want 7 detuned saws playing one note in unison, you would need
7*220= 1540sines. If you want to play a chord of 4 notes with it and in that range, you would need approximately
1540*4= 6160sines to sum at the same time, each multiplied element-wise by some frequency response function to evaluate in parallel on a 6160 sized vector (the frequencies), all that knowing that one might play notes in a fast way so you can’t compute things too much in advance. Do you think it could work? You know more than me when it comes to computational time (I have a mathematical background and have done a lot of programming but only as a hobby so still a lot to learn).
@JonB I think I need to take a step back and put in practice all this ideas we mentioned. There is a lot of information in there, and I have a lot to learn (c_types, objc_utiles , more about numpy, Apple Core Audio) and a lot of ideas to try (Standard DSP, parallel approximation, convolution, sum of sines, pre-buffering) and I feel like I need to catch up with all that before we keep thinking about new ideas (the more ideas we get, the more work it will take to try them all) ;)
Also, I will try to code it first myself (as a learning exercise) before looking more at your code (as a reference/solution). I learn better that way.
I will get back to you when all of this will be done. I am under the impression that you are also interested in coding a synth with Pythonista (correct me if I am wrong), but it seems that your vision is to have a complete modular environment while I am (right now) really trying to do something minimalistic and customized for my personal (and subjective) preferences, so we will probably end up with two different codes :)
I am not coding a synth. Mostly I have been interested in pushing the boundaries of high performance on pythonista, as a fun exercise, and trying to understand these various libraries. Making the iPad bleep is also kinda fun ;)
@JonB Coming back sooner than I thought :)
I made an interesting experiment with your codes and there is some phenomenon I am not sure I understand right.
If you take your audiounittest.py code (the very first one) and play the sine by modulating the frequency very fast on the whole range, you will notice quantized modulation. Now if you take your other code: audiounittest2.py, set it to a sine, and do the same thing. It’s a perfectly smooth frequency modulation. Do you hear the difference? It’s not really audio glitches like caused by overheading. It’s really « quantized » frequencies.
Here is what I tried to fight that. In the render() method of audiounittest.py, I stored the last used frequency in the previous render() call in a self.previous_frequency attribute.
Then, at the beginning of render(), I assign self.previous_frequency to a prev_freq variable and the frequency from self.sound[touch_id] to a current_freq variable. Then, during the « for frame in range(numFrames) » loop, I interpolate between prev_freq and current_freq. It killed the frequency quantization effect. That’s the only way I could get that perfectly smooth modulation I was hearing in audiounittest2.py.
The problem was solved but I still wanted to understand what was the issue.I first thought it had to do with maybe the touch_moved() method getting slowed down in audiounittest.py by the less efficient implementation compared to audiounittest2.py, but I just timed them and both touch_moved are around 110Hz. In other words, the sounds[touch_id] attribute changes as often in audiounittest.py as in audiounittest2.py. So it doesn’t explain the quantization.
Here is my current guess.
In audiounittest2.py, you compute the samples perfectly without missing any frequency values because the touch_moved() method is actually calling the generator and sending to it the frequency as a parameter. So no frequency is missed by the generator.
On the other hand, in audiounittest.py, the render() method generally fills the buffer faster than the duration of the buffer itself. In other words, there is always time when the render() method « waits » before filling the buffer again. The consequence is that when it fills the buffer it actually only accesses the first few frequency values happening during this very short time and use them to fill the whole buffer. Then, during the waiting time, it misses all the other frequency values. Btw, I don’t think it falls under the scope of overheading. To me overheading is the opposite (the render() method being to slow), but here it would be kind of too fast.
What do you think about this? Am I understanding the issue correctly?
Check how many samples render is asking for (display numFrames). In my version, it asks for 1024 at a time for 44100 sample rate. So, if you move frequency over 8000 Hz in half a sec, (22000 samples), it will be quantized into 8000/22 Hz chunks.
The buffered approach updates the buffer (up to present time, at least using time.perf_counter) for every touch moved, and within update. So over 0.5 sec you might get several hundred touch events, and updates every few msec.
https://gist.github.com/6ccd9ad8ba95c373ec7d76ceaf9061bc has some minor corrections, adds some diagnostic prinouts, and pulled all the ctypes garbage into a separate file.
So, even if you don't use the modular generator approach, you might consider using a custom generator (i.e subclass ToneGenerator and handle the logic within the buffer filling methods.
It is also theoretically possible to force the audiounit to ask for data more frequently. https://developer.apple.com/documentation/audiotoolbox/1534199-generic_audio_unit_properties/kaudiounitproperty_maximumframesperslice?language=objc
maxFrames=c_uint32(256) err = AudioUnitSetProperty(toneUnit, kAudioUnitProperty_MaximumFramesPerSlice, kAudioUnitScope_Global, 0, byref(maxFrames), sizeof(maxFrames));
however this didnt't work when i tried.
I agree with you about the 8000/22Hz computation but it’s only true if my assumption (the render() method filling the buffer way faster than the buffer’s duration) is true.
- if the render() method was taking 0.02s to fill a 0.02s buffer, then, as it accesses the sounds[touch_id] attribute at each iteration of the « for frame in range(numFrames) » loop, it should be accessing the correct frequency values in real time and at the right time and fill a 0.02s buffer with frequency values corresponding to a 0.02s time of modulation, so there shouldn’t occur quantizing, at least not more than in audiounittest2.py, knowing that touch_moved updates occurred at 110Hz even for the fastest moves in both codes, so the quantization should happen at a 110Hz rate (which is not noticeable and appears as smooth) (Even if you remove the computation in the update part of audiounittest2.py you won’t notice quantization)
- If on the contrary, the render() method takes 0.001s to fill a 0.02s buffer, then, even if it accesses the sounds[touch_id] attribute at each iteration of the « for frame in range(numFrames) » loop, it will be doing so during a 0.001s period, thus accessing frequency values corresponding to such a small period of time and using it to fill the whole 0.02s buffer. So it will be almost as if it only gets the very first frequency of the 0.02s and use it for the whole chunk resulting in a 1/0.02 = 50Hz quantization. Somehow this is noticeable on fast modulation (like 30fps vs 15fps in graphics when there is fast motion, I guess).
Sorry for the redundancy but I wanted to be more precise in what I meant.
Also, I do intend to have a prebuffered approach in the future, I am just studying the differences in order to learn and figure out more exactly how the render() thread works.
@JonB , just to keep you up to date: I finally took my initial non-real-time synth and merged it with audiounittest. It wasn’t long to do because my synth was actually already computing 60Hz chunks of sound data in the update method (of a Scene), appending it to an out array. I had done that in the past back when I was trying to achieve real time by continuously writing in a .wav file at each update (that’s when I realized that it couldn’t really be done right and came here to create this thread about the audio circular buffer functionality).
So it was already all set up for real time and the circular buffer! The only thing I had to do was to not write in a .wav file, and place the out array in the AudioRenderer’s attributes, and have the render() method start reading it after waiting a fixed latency attribute. Weirdly, latency=0 actually works glitchless for me :) I guess that’s because the first render() doesn’t work, in which case the first audio chunk is kept null by default, and then the render() thread naturally takes a delay of one chunk behind the update() method.
Of course, later I will have to use some kind of deque or circular array for the out attribute because right now it’s basically recording the whole performance without deleting anything, but I am already really happy to have been able to just take my non-real time synth code from before and almost just copy/paste it in audiounittest.py! When you think about it, it’s very similar to what I had to do with the smudge tool: I almost didn’t change my code and basically merged it with your IOSurface wraper! It’s interesting how one can just plug a pure Python script to these features (IOSurface wraper and AudioRenderer) and work perfectly :)
And I still intend to take advantage of numpy in multiple places.
I remembered reading about the Accelerate framework. Might have some useful bits for you
In particular: vector polynomial evaluation (though i image numpy is competitive here), IIR filter in biquad form, and some other goodness that would be in scipy, but not numpy.
@JonB Sorry for the late answer. Thank you for this. I won’t have the time to look at it for a while because I have put other stuff on pause for too long for this project and now I have to catch up.
Right now everything will just feel like a bonus/improvement to my current code because it’s already working very well on my device.
Btw, as an experiment I tried using numpy to compute sawtooths as sums of sines (in real time) and it gets quickly hard to keep up for my device. A few notes at the same time and there was overhead. To be honest I didn’t try every possibilities. I basically summed the sines with according weights by stacking them as rows in a matrix and left-multiplying this matrix by a row vector of the weights.
I think the only reasonable way to use the sum of sines decomposition is to precompute wavetables and then use them.