Optimizing latency of an Arduino MIDI controller

Some of the feedback from first user testing of my friend’s Hang-like electronic music instrument was that the latency was too high. How do we bring it down to an acceptable level?

A MIDI controller using capacitive touch sensors for triggering. An Arduino board processes the sensor data and sends MIDI notes over USB to a PC or mobile device. A synthesizer on the computer turns the notes into sound.

Testing latency

For an interactive system like this, what matters is the performance experienced by the user. For a MIDI controller that means the end-to-end latency, from hitting the pad until the sound triggered is heard. So this is what we must be able to observe in order to evaluate current performance and the impact of attempted improvements. And to have concrete, objective data to go by, we need to measure it.

My first idea was to use a high-speed camera, using the video image to determine when pad is hit and the audio to detect when sound comes from the computer. However even at 120 FPS, which some modern cameras/smartphones can do, there is 8.33 ms per frame. So to find when pad was hit with higher accuracy (1ms) would require using multiple frames and interpolating the motion between them.

Instead we decided to go with a purely audio-based method:

Test setup for measuring MIDI controller end2end latency using audio recorded with smartphone.

  • The microphone is positioned close to the controller pad and the output speaker
  • The controller pad is tapped with the finger quickly and hard enough to be audible
  • Volume of the output was adjusted to be roughly same level as sound of physically hitting the pad
  • In case the images are useful for understanding the recorded test, video is also recorded
  • The synthesized sound was chosen to be easily distinguished from the thud of the controller pad

To get access to more settings, the open-source OpenCamera Android app was used. Setting a low video bitrate to save space, and enabling macro-mode for focusing close objects easier. For synthesizing sounds from the MIDI signals we use LMMS, a simple but powerful digital music studio.

Then we open the video in Audacity audio editor to analyze the results. Using Effect->Amplify to normalize the audio to -1db makes it easier to see the waveforms. And then we can manually select and label the distance between the starting points of the sounds to get our end-to-end latency.

Raw sound data, data with normalized amplitude and measured distance between the sound of tapping the sensor and the sound coming from speakers.

How good is good enough?

We now know that the latency experienced by our testers was around 137 ms. For reference, when playing a (relatively slow) 4/4 beat at 120 beats per minute, the distance between each 16th notes is 125 ms. In the following soundclip the kickdrum is playing 4/4 and the ‘ping’ all 16 16th notes.

So the latency experienced would offset the sound by more than one 16th note! We can understand that this would make it tricky to play.

For professional level audio, less than <10 ms is a commonly cited as the desired performance, especially for percussion. From Action-Sound Latency: Are Our Tools Fast Enough?

Wessel and Wright suggested that digital musical
instruments should aim for latency less than 10ms [22]

Dahl and Bresin [3] found that in a system
with latency, musicians execute their gestures ahead of the
beat to align the sound with a metronome, and that they
can maintain synchronisation this way up to 55ms latency.

Since the instrument in question is going to be a kit targeted at hobbyists/amateurs, we decided on an initial target of <30ms.

Sources of latency

Latency, like other performance issues, is a compounding problem: Each operation in the chain adds to it. However usually a large portion of the time is spent in a small parts of the system, so an important part of optimization is to locate the areas which matter (or rule out areas that don’t).

For the MIDI controller system in question, a software-centric view looks something like:

A functional view of the system and major components that may contribute to latency. Made with Flowhub

There are also sources of latency outside the software and electronics of the system. The capacitive effect that the sensor relies on will have a non-zero response time, and it takes time for sound played by the speakers to reach our ears. The latter can quickly be come significant; at 4 meters the delay is already over 10 milliseconds.

And at this time, we know what the total latency is, but don’t have information about how it is divided.

With simulation-hardened Arduino firmware

The system tested by users was running the very first hardware and firmware version. It used a an Arduino Uno. Because the Uno lacks native USB, a serial->MIDI bridge process had to run on the PC. Afterwards we developed a new firmware, guided by recorded sensor data and host-based simulation. From the data gathered we also decided to switch to a more sensitive sensor setup. And we switched to Arduino Leonardo with native USB-MIDI.

Latency with new firmware (with 1 sensor) was reduced by 50 ms (35%).

This firmware also logs how long each sensor reading cycle takes. It was under 1 ms for the recorded single-sensor setup. The sensor readings went almost instantly from low to high (1-3 cycles). So if the sensor reading and triggering takes just 3 ms, the remaining 84 ms must be elsewhere in the system!

Low-latency audio, a hard real-time problem

The two other main areas of the system are: the USB/MIDI communication from the Arduino to the PC, and the sound synthesis/playback. USB MIDI should generally be relatively low-latency, and it is a subsystem which we cannot influence so easily – so we focus first on the sound aspects.

Since a PC must be able to do multi-tasking, audio is processed in chunks: a buffer of N samples. This allows some flexibility. However if processing is interruptedfor toolong or too often, the buffer may not be completely filled. The resulting glitch is usually heard as a pop or crackle. The lower latency we want, the smaller the buffer, and the higher chance that something will interrupt for too long. At 96 samples/buffer of 48kHz samplerate, each buffer is just 2 milliseconds long.

With JACK on on Linux

I did the next tests on Linux, since I know it better than Windows. Configuring JACK to 256 samples/buffer, we see that the audio configuration does indeed have a large impact.

Latency reduced to half by configuring Linux with JACK for low-latency audio.

 

With ASIO4ALL on Windows

But users of the kit are unlikely to use Linux, so a solution that works with Windows is needed (at least). We tried all the different driver options in LMMS, switching to Hydrogen drum machine, as well as attempting to use JACK on Windows. None of these options worked well.
So in the end we tried going with ASIO, using the ASIO4LL replacement drivers. Since ASIO is proprietary LMMS/PortAudio does not support it out-of-the-box. Instead you have to manually replace the PortAudio DLL that comes with LMMS with a custom one 🙁 *nasty*.

With ASIO4ALL we were able to set the buffer size as low as 96 samples, 2 buffers without glitches.

ASIO on Windows achieves very low latencies. Measurement of single sensor.

Completed system

Bringing back the 8 other sensors again adds around 6 ms to the sensor reading, bringing the final latency to around 20ms. There are likely still possibilities for significant improvements, but the target was reached so this will be good enough for now.

A note on jitter

The variation in latency of a audio system is called jitter. Ideally a musical instrument would have a constant latency (no jitter). When a musical instrument has significant amounts of jitter, it will be harder for the player to compensate for the latency.

Measuring the amount of jitter would require some automated tools for the audio analysis, but should otherwise be doable with the same test setup.
The audio pipeline should have practically no variation, but the USB/MIDI communication might be a source of variation. The CapacitiveSensor Arduino library is known to have variation in sensor readout time, depending on the current capacitance of the sensor.

Conclusions

By recording audible taps of the sensor with a smartphone, and analyzing with a standard audio editor, one can measure end-to-end latency in a tactile-to-sound instrument. A combination of tweaking the sensor hardware layout, improving the Arduino firmware, and configuring PC software for low-latency audio was needed to aceive acceptable levels of latency. The first round of improvements brought the latency down from an ‘almost unplayable’ 134 ms to a ‘hobby-friendly’ 20 ms.

Comparison of latency betwen the different configurations tested.

 

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Improved drawing performance in MyPaint brush engine

A first set of performance improvements for the brush engine has just landed in MyPaint master. The goals for this work for me were, in priority: a) Making sure that moving to a GEGL backend in MyPaint does not reduce performance, b) Improve performance when integrating the MyPaint brush engine in other applications, and lastly c) Improving the performance in MyPaint itself.

TL;DR: * Users of the soon-to-be-released MyPaint 1.1 should experience about 15% faster drawing of strokes for medium to big brushes. * Switching to the GEGL based backed for MyPaint 1.2 is now both feasible and highly desirable from a performance perspective.


Optimizations

The optimizations are implemented through three complimentary strategies:

1. Deferred data access to minimize fetching and updating of tiles

All dab drawing operations that happen as a result of a motion update event are queued up. When the brush engine has calculated where all dabs should go, tiles are fetched, all dabs drawn and the tiles updated. This in contrast to before where each dab drawing operation would fetch and update tiles.

2. Coarse grained parallelism using multi-threading via OpenMP directives

The tiles to be processed are divided evenly between processing threads (one per core). Each tile is processed completely independent of other tiles, so there is no locking or synchronization in the drawing code. The tile backing store must naturally be thread-safe and may ensure this using locks.

3. Fine grained parallelism using SSE via GCC auto-vectorization

Within each tile we attempt to make use of auto-vectorization to create the brush dab mask and do the composition of the dab onto the tile. Currently this is only implemented for a part of the mask calculation.

Results

Details of the results and how they can be reproduced is found in the original email thread.

Gains for MyPaint 1.1

Starting with the lowest priority goal, but the most relevant to users; performance impact on MyPaint right now.

Surface drawing results for existing Python-based backend

In terms of raw speed of drawing brushes to onto the underlying surface, speedups range from 20% to 50% for larger brushes (16 px+). This sets an upper boundary for the speedup perceived by the user.

Looking at the UI-enabled benchmarks of MyPaint, which is doing everything a normal application instance does, including layer compositing and rendering to screen, around 15% speedup was observed.  As the UI benchmark only tests a single brush at size=8.0px, it is possible that larger brushes will a higher speedup.

Users of the soon-to-be-released MyPaint 1.1 should experience about 15% faster drawing of strokes for medium to big brushes.

Note that the backend in use does not make use of the multi-threading introduced by (2) due to the tile store not being thread-safe and that it already had a cache to mitigate the problem fixed by (1).
Note

GEGL-based backend results, outlook for MyPaint 1.2

Surface drawing results, GEGL versus Python-based surface.
Test results by Till Hartmann on his Phenom II X6.

So in terms of raw surface rendering speed, the GEGL based backend is now significantly faster than the Python-based one. With 1 and 2 threads it is respectively up to 25% and 100% faster for big brush sizes. With 6 threads, it can be up to 4 times faster.

Switching to the GEGL based backed for MyPaint 1.2 is now both feasible and highly desirable from a performance perspective.

Note that to see UI performance increases approaching the raw surface drawing performance increase we may also need to do the layer compositing multi-threaded.

Gains in other applications

I’m trying to convince the Krita guys to update to the new version and to provide some feedback on the impact. Other consumers of the MyPaint brush engine do not tend to communicate much with us (some are proprietary).
I have strong hopes that (1) should increase their performance radically as their tile get/set cost is significantly higher than in the MyPaint case: They need to convert between the internal Krita and the MyPaint brush engine working colorspace each time. They may also be able to enable multi-threading and see speedups similar to the GEGL-based backend as a result.

Future Work

This is only lays the groundwork of better optimized MyPaint brush engine, many areas have room for improvement. For one only a small subset of the heavy code is vectorized. There may be inner loops that can be tweaked. It may be that, with a different tile access pattern compared to before, a different tile size would be more ideal. Perhaps doing the expensive calculation of the brush dab could be avoided some times by caching them… Thinking bigger, one could move all the drawing (and rendering) to the GPU.

More details on these ideas can be found here. If you are interested in working on any of it, get in touch and start hacking!

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