This is a quick PSA for those of you using the GStreamer binary builds for Android.
With the Android NDK r12, the default behaviour while building native code changed from building for armeabi to building for all ABIs. So if your app doesn’t specify APP_ABI in its Application.mk, you will now get an error about unsupported architectures. This was tracked as bug 770631.
The idea behind this change is that your Android app should ship versions of your native code for all supported architectures as a “universal” build, so it is accessible to as many devices as possible.
To deal with this, we now provide a universal tarball which contains binaries for all archiectures that we support. This is currently ARM, ARMv7-A, ARMv8-A (64-bit), x86, and x86-64. That leaves MIPS and MIPS64 that are not currently supported.
If you’ve been using the GStreamer Android binaries before GStreamer 1.9.2, then you should start using the universal tarball rather than the architecture-specific tarball. You will need minor updates to your native build, like we made to the player example. You probably want to put the gstAndroidRoot variable in ~/.gradle/gradle.properties instead, though.
As Sebastian announced, assuming all goes well with the universal tarballs, we will stop shipping the per-arch tarballs — they are redundant, and just take up CI and disk resources.
There are some things that I’d like for us to be able to do better. The first is that Android Studio doesn’t pick up native code with our current build approach. This is a limitation of the Android Gradle NDK plugin, which doesn’t support a custom build. This should change with Android Studio 2.2.
I would also like to integrate better with Android Studio — either be able to specify the GStreamer Android binary path in the UI (like you do for the SDK/NDK), or better yet, have it be possible to specify the dependency in Gradle, and have it be automatically pulled from the Internet. If any of you are familiar with how we can do this, please shout out!
In case you missed it — we got PulseAudio 9.0 out the door, with the echo cancellation improvements that I wrote about. Now is probably a good time for me to make good on my promise to expand upon the subject of beamforming.
As with the last post, I’d like to shout out to the wonderful folks at Aldebaran Robotics who made this work possible!
Beamforming
Beamforming as a concept is used in various aspects of signal processing including radio waves, but I’m going to be talking about it only as applied to audio. The basic idea is that if you have a number of microphones (a mic array) in some known arrangement, it is possible to “point” or steer the array in a particular direction, so sounds coming from that direction are made louder, while sounds from other directions are rendered softer (attenuated).
Practically speaking, it should be easy to see the value of this on a laptop, for example, where you might want to focus a mic array to point in front of the laptop, where the user probably is, and suppress sounds that might be coming from other locations. You can see an example of this in the webcam below. Notice the grilles on either side of the camera — there is a microphone behind each of these.
Webcam with 2 mics
This raises the question of how this effect is achieved. The simplest approach is called “delay-sum beamforming”. The key idea in this approach is that if we have an array of microphones that we want to steer the array at a particular angle, the sound we want to steer at will reach each microphone at a different time. This is illustrated below. The image is taken from this great article describing the principles and math in a lot more detail.
Delay-sum beamforming
In this figure, you can see that the sound from the source we want to listen to reaches the top-most microphone slightly before the next one, which in turn captures the audio slightly before the bottom-most microphone. If we know the distance between the microphones and the angle to which we want to steer the array, we can calculate the additional distance the sound has to travel to each microphone.
The speed of sound in air is roughly 340 m/s, and thus we can also calculate how much of a delay occurs between the same sound reaching each microphone. The signal at the first two microphones is delayed using this information, so that we can line up the signal from all three. Then we take the sum of the signal from all three (actually the average, but that’s not too important).
The signal from the direction we’re pointing in is going to be strongly correlated, so it will turn out loud and clear. Signals from other directions will end up being attenuated because they will only occur in one of the mics at a given point in time when we’re summing the signals — look at the noise wavefront in the illustration above as an example.
Implementation
(this section is a bit more technical than the rest of the article, feel free to skim through or skip ahead to the next section if it’s not your cup of tea!)
The devil is, of course, in the details. Given the microphone geometry and steering direction, calculating the expected delays is relatively easy. We capture audio at a fixed sample rate — let’s assume this is 32000 samples per second, or 32 kHz. That translates to one sample every 31.25 µs. So if we want to delay our signal by 125µs, we can just add a buffer of 4 samples (4 × 31.25 = 125). Sound travels about 4.25 cm in that time, so this is not an unrealistic example.
Now, instead, assume the signal needs to be delayed by 80 µs. This translates to 2.56 samples. We’re working in the digital domain — the mic has already converted the analog vibrations in the air into digital samples that have been provided to the CPU. This means that our buffer delay can either be 2 samples or 3, not 2.56. We need another way to add a fractional delay (else we’ll end up with errors in the sum).
There is a fair amount of academic work describing methods to perform filtering on a sample to provide a fractional delay. One common way is to apply an FIR filter. However, to keep things simple, the method I chose was the Thiran approximation — the literature suggests that it performs the task reasonably well, and has the advantage of not having to spend a whole lot of CPU cycles first transforming to the frequency domain (which an FIR filter requires)(edit: converting to the frequency domain isn’t necessary, thanks to the folks who pointed this out).
Now it’s time for a confession. I’m a plumber, not a DSP ninja. My delay-sum beamformer doesn’t do a very good job. I suspect part of it is the limitation of the delay-sum approach, partly the use of an IIR filter (which the Thiran approximation is), and it’s also entirely possible there is a bug in my fractional delay implementation. Reviews and suggestions are welcome!
A Better Implementation
The astute reader has, by now, realised that we are already doing a bunch of processing on incoming audio during voice calls — I’ve written in the previous article about how the webrtc-audio-processing engine provides echo cancellation, acoustic gain control, voice activity detection, and a bunch of other features.
Another feature that the library provides is — you guessed it — beamforming. The engineers at Google (who clearly are DSP ninjas) have a pretty good beamformer implementation, and this is also available via module-echo-cancel. You do need to configure the microphone geometry yourself (which means you have to manually load the module at the moment). Details are on our wiki (thanks to Tanu for that!).
How well does this work? Let me show you. The image below is me talking to my laptop, which has two microphones about 4cm apart, on either side of the webcam, above the screen. First I move to the right of the laptop (about 60°, assuming straight ahead is 0°). Then I move to the left by about the same amount (the second speech spike). And finally I speak from the center (a couple of times, since I get distracted by my phone).
The upper section represents the microphone input — you’ll see two channels, one corresponding to each mic. The bottom part is the processed version, with echo cancellation, gain control, noise suppression, etc. and beamforming.
WebRTC beamforming
You can also listen to the actual recordings …
… and the processed output.
Feels like black magic, doesn’t it?
Finishing thoughts
The webrtc-audio-processing-based beamforming is already available for you to use. The downside is that you need to load the module manually, rather than have this automatically plugged in when needed (because we don’t have a way to store and retrieve the mic geometry). At some point, I would really like to implement a configuration framework within PulseAudio to allow users to set configuration from some external UI and have that be picked up as needed.
Nicolas Dufresne has done some work to wrap the webrtc-audio-processing library functionality in a GStreamer element (and this is in master now). Adding support for beamforming to the element would also be good to have.
The module-beamformer bits should be a good starting point for folks who want to wrap their own beamforming library and have it used in PulseAudio. Feel free to get in touch with me if you need help with that.
While I did mention a while back (almost two years ago, wow) that I was taking a break, I realised recently that I hadn’t posted an update from when I started again.
For the last year and a half, I’ve been providing freelance consulting around PulseAudio, GStreamer, and various other directly and tangentially related projects. There’s a brief list of the kind of work I’ve been involved in.
If you’re looking for help with PulseAudio, GStreamer, multimedia middleware or anything else you might’ve come across on this blog, do get in touch!
I know it’s been ages, but I am now working on updating the webrtc-audio-processing library. You might remember this as the code that we split off from the webrtc.org code to use in the PulseAudio echo cancellation module.
This is basically just the AudioProcessing module, bundled as a standalone library so that we can use the fantastic AEC, AGC, and noise suppression implementation from that code base. For packaging simplicity, I made a copy of the necessary code, and wrote an autotools-based build system around that.
Now since I last copied the code, the library API has changed a bit — nothing drastic, just a few minor cleanups and removed API. This wouldn’t normally be a big deal since this code isn’t actually published as external API — it’s mostly embedded in the Chromium and Firefox trees, probably other projects too.
Since we are exposing a copy of this code as a standalone library, this means that there are two options — we could (a) just break the API, and all dependent code needs to be updated to be able to use the new version, or (b) write a small wrapper to try to maintain backwards compatibility.
I’m inclined to just break API and release a new version of the library which is not backwards compatible. My rationale for this is that I’d like to keep the code as close to what is upstream as possible, and over time it could become painful to maintain a bunch of backwards-compatibility code.
A nicer solution would be to work with upstream to make it possible to build the AudioProcessing module as a standalone library. While the folks upstream seemed amenable to the idea when this came up a few years ago, nobody has stepped up to actually do the work for this. In the mean time, a number of interesting features have been added to the module, and it would be good to pull this in to use in PulseAudio and any other projects using this code (more about this in a follow-up post).
So if you’re using webrtc-audio-processing, be warned that the next release will probably break API, and you’ll need to update your code. I’ll try to publish a quick update guide when releasing the code, but if you want to look at the current API, take a look at the current audio_processing.h.
p.s.: If you do use webrtc-audio-processing as a dependency, I’d love to hear about it. As far as I know, PulseAudio is the only user of this library at the moment.
Update: gst-build is the current way to build GStreamer for developement. I’m leaving the post up for posterity, but other than the note on getting dependencies, you should not be using this.
One of the first tools that you should get if you’re hacking with GStreamer or want to play with the latest version without doing evil things to your system is probably the gst-uninstalled script. It’s the equivalent of Python’s virtualenv for hacking on GStreamer. :)
The documentation around getting this set up is a bit frugal, though, so here’s my attempt to clarify things. I was going to put this on our wiki, but that’s a bit search-engine unfriendly, so probably easiest to just keep it here. The setup I outline below can probably be automated further, and comments/suggestions are welcome.
First, get build dependencies for GStreamer core and plugins on your distribution. Commands to do this on some popular distributions follow. This will install a lot of packages, but should mean that you won’t have to play find-the-plugin-dependency for your local build.
(the -master suffix for the script is important to how the script works)
Enter the uninstalled environment:
$ ~/bin/gst-master
(this puts you in the directory with all the checkouts, and sets up a bunch of environment variables to use your uninstalled setup – check with echo $GST_PLUGIN_PATH)
Some of you might have been following all the brouhaha over Popcorn Time. I won’t get into the arguments that can be made for and against at the moment.
While poking around at what it was that Popcorn Time was doing, I stumbled upon peerflix, a Node.js-based application that takes a .torrent file that points to one big video file, and presents that as an HTTP stream. It has its own BitTorrent implementation where it prioritises early chunks of the file so that it is possible to start watching the video before the entire file has been downloaded. It also seeds the file while the video is being watched locally.
Seeing as I was at the GStreamer Hackfest in Munich when this came up in discussions, it seemed topical to have a GStreamer element to wrap this neat bit of functionality. Thus was peerflixsrc born. This is a simple source element that takes a URI to a torrent file (something like torrent+http://archive.org/some/video.torrent), fires up peerflix in the background, and provides the data from the corresponding HTTP stream. Conveniently enough, this can be launched using playbin or Totem (hinting at the possibilities of what can come next!). Here’s what it looks like…
Screenshot of Totem playing a torrent file directly using peerflixsrc
The code is available now. To use it, build this copy of gst-plugins-bad using your favourite way, make sure you have peerflix installed (sudo npm install -g peerflix), and you’re good to go.
This is not quite mature enough to go into upstream GStreamer. The ugliest part is firing up a Node.js server to make this work, not the least because managing child processes on Linux is not the prettiest code you can write. Maybe someone wants to look at rewriting the torrent bits from peerflix in C? There don’t seem to be any decent C-based libraries for this out there, though.
In the mean time, enjoy this, and comments / patches welcome!
Last weekend, I was at the GStreamer Hackfest in Munich. As usual, it was a blast — we got much done, and it was a pleasure to meet the fine folks who bring you your favourite multimedia framework again. Thanks to the conference for providing funding to make this possible!
My plan was to work on making Totem’s support for passthrough audio work flawlessly (think allowing your A/V receiver to decode AC3/DTS if it allows it, with more complex things coming the future as we support it). We’ve had the pieces in place in GStreamer for a while now, and not having that just work with Totem has been a bit of a bummer for me.
The immediate blocker so far has been that Totem needs to add a filter (scaletempo) before the audio sink, which forces negotiation to always pick a software decoder. We solved this by adding the ability for applications to specify audio/video filters for playbin to plug in if it can. There’s a now-closed bug about it, for the curious. Hopefully, I’ll get the rest of the work to make Totem use this done soon, so things just work.
Now the reason that didn’t happen at the hackfest is that I got a bit … distracted … at the hackfest by another problem. More details in an upcoming post!
Four years and what seems like a lifetime ago, I jumped aboard the ship Collabora Multimedia, and set sail for adventure and lands unknown. We sailed through strange new seas, to exotic lands, defeated many monsters, and, I feel, had some positive impact on the world around us. Last Friday, on my request, I got dropped back at the shore.
I’ve had an insanely fun time at Collabora, working with absurdly talented and dedicated people. Nevertheless, I’ve come to the point where I feel like I need something of a break. I’m not sure what’s next, other than a month or two of rest and relaxation — reading, cycling, travel, and catching up with some of the things I’ve been promising to do if only I had more time. Yes, that includes PulseAudio and GStreamer hacking as well. :-)
That’s right — PulseAudio will be participating in the Google Summer of Code again this year! We had a great set of students and projects last year, and you’ve already seen some their work in the last release.
There are some more details on how to get involved on the mailing list. We’re looking forward to having another set of smart and enthusiastic new contributors this year!
p.s.: Mentors and students from organisations (GStreamer and BlueZ, for example), do feel free to get in touch with us if you have ideas for projects related to PulseAudio that overlap with those other projects.
Yay, we just released PulseAudio 3.0! I’m not going to rehash the changelog that you can find in the release announcement as well as the longer release notes.
I would like to thank the 36 contributors over the last 6 months who have made this release what it is and continue to demonstrate what a vibrant community we have!
