Hi, I'm Joren. Welcome to my website. I'm a researcher in the field of Music Informatics, Music Information Retrieval, and Computational Ethnomusicology. Here you can find a record of my research and other projects I have been working on. Learn more »


Joren Six
University Ghent, IPEM

~ OSC in Matlab on Windows, Linux and Mac OS X using Java

matlab logoThis post explains how to receive OSC in a MatLab environment. It uses a platform independent Java library which should work on 64 and 32 bit versions of Windows, Unix and Mac OS X. Using Java makes installation relatively easy compared with other solutions.

The most used method to get OSC-messages in Matlab can be found here. This method uses a library called liblo which needs to be configured (compiled) correctly on your system. Especially on Windows this can be problematic. A brave soul documented his quest to get OSC working with Matlab on Windows here. Obviously not for the faint of heart.

An alternative way leverages the Matlab facilities to run Java. Since there is a Java OSC library available (JavaOSC on github) it is relatively easy to bridge the two. To make it easy I have implemented some glue code and provide an easy to use Jar-library here. Using the code is simple:

How to make Matlab receive OSC-messages

  1. Download the JavaOSCtoMatlab Java library and store it in an easy to remember directory.
  2. Download the example Matlab OSC client Script and store it in the same directory. The client is included below as well.
  3. Start Matlab, modify the client script to fit your needs. You probably need to change the OSC method to listen to and the OSC port. Also make sure that the cd command points to the directory with the downloaded jar-file.
  4. Run the client script and receive your OSC messages.

Note that there are three ways to receive the payload of a message. They are returned by the Java code as either Object[], double[] or String[]. The last two are automatically understood by Matlab, so they are more easy to work with. Respectively to get the message data you need to call either osc_listener.getMessageArguments(), osc_listener.getMessageArgumentsAsDouble(), osc_listener.getMessageArgumentsAsString().

I hope this is useful to some…


% Check your java version 1.6+ should be ok
version -java
% Load the jar file
% Import the needed java packages
import com.illposed.osc.*;
import java.lang.String

% defines the OSC port to listen to
receiver =  OSCPortIn(4000);
% defines the OSC method to listen to
osc_method = String('/ECG');
osc_listener = MatlabOSCListener();

%infinite loop, receiving all non empty messages 
    struct = osc_listener.getMessageArgumentsAsDouble();
     if ~isempty(struct)


~ Measuring Audio Output Latency on Android Lollipop using an Arduino

This post explains how to measure audio output latency on Android devices. To measure audio latency USB-OTG and an Arduino is used. In the process it documents audio output latency on an LG Nexus 5 device running the most recent version of Android, which currently is Lollipop (5.0).

Audio latency is an important aspect of a system, especially if it is used for real-time sonification or for musical applications. Audio latency is the, preferably short, delay between audio entering a system and emerging from a system. Audio output latency is the time it takes between a signal (e.g. a button pressed) and when audio emerges. For sonification purposes audio output latency is more interesting than round-trip audio latency.

Android systems are often portable, generally available and relatively cheap. Android offers an attractive platform to develop sonifications or musical applications for. Unfortunately, audio latency on Android has not been a priority in the first versions. With Android 4.1 things started to change but due to hard- and software fragmentation it is still hard to find how much audio latency is expected. Even if the exact model (e.g. Nexus 5) and software version (stock Android 5.0) is known, exact numbers are, so it seems, nowhere to be found. For more information on the internal changes that make low latency audio on Android possible, watch the talk on High Performance Audio from the 2013 Google I/O conference. Also note the lack of exact latency numbers in that talk. It is a very enjoyable talk by two Google engineers going after the culprits of high latency in true Sherlock/dr. Watson style.

Since audio output latency is generally not documented and since it is an important factor to decide if Android is a viable platform for real-time sonification or musical applications it needs to be measured. One way of measuring audio output latency on Android is documented by the people of Google. Unfortunately, the approach is not easily reproducible since it needs a custom circuit board, an oscilloscope and there is no source code available. Below a reproducible way to measure audio output latency for Android is documented.

An Arduino, an Android device, an USB-OTG cable and a butchered mini-jack audio cable are needed together with the software provided here. Optionally, a data acquisition module can be used to visualize the signals. The measurement system works as follows:

  1. An Arduino sends a signal over USB. The time at which the signal is send is stored for later use.
  2. An Android device, connected to the Arduino via an USB-OTG-cable, receives the signal.
  3. The Android device responds as quickly as possible, with the lowest latency as possible, by emitting a sound.
  4. The sound is captured on an analog input port of the Arduino, via the mini-jack cable. The time the sound appears on the Arduino is stored.
  5. By comparing the time when the signal was send with the time when the sound arrived, the audio output latency is measured and reported.

The previous steps are repeated every second to gain insights into the variability of the measurements. To generate microsecond accurate timing interrupts are used on the Arduino. For visualisation, a digital pin is toggled every time the Arduino sends a signal. The Arduino sketch is attached to this post, as is the source code for the Android application. An already compiled APK is also available. With some luck – a recent Android version is needed, your device should support USB-OTG – it might work on your device.


Using the OpenSL ES native interface on a Nexus 5 with Lollipop installed the USB input to audio output latency is on average about 48 milliseconds. There is some variability but it is usually within 15 milliseconds. For music applications this latency is not great but, depending on the application, acceptable. For expert drummers latency should be in the range of 20ms but for many sonification tasks, 50ms suffices. It is clear that Android will never be able to compete with purpose built hardware running a real time operating system like Axoloti (Audio roundtrip latency 2ms, usb-audio 1.6ms) but for a general purpose device the measured latency is significantly better than what I expected (around 100ms).

The non-native audio interface is a lot slower. I have measured an average latency of about 85ms and a much larger variability (25ms).

With this post I hope others will report the latency for their devices as well, so that buyers that are interested in a low-latency Android devices can make an informed decision.

  • The DAC used.

    The DAC used.

  • The latency visualized.

    The latency visualized.

  • Arduino and the DAC

    Arduino and the DAC

  • Arduino wiring.

    Arduino wiring.

  • Onsets and audio visualized using a DAC and a Java program.

    Onsets and audio visualized using a DAC and a Java program.

  • Result on Android.

    Result on Android.

~ Axoloti: a digital audio platform for makers

Currently, there is a crowd-funding campaign ongoing about Axoloti . Axoloti is a very cool project by Johannes Taelman. It is a stand alone audio processing unit that can be used as a synthesizer, groovebox, guitar effect pedal, as a part of a sound installation, or for about any other audio application you can think of.

Axeloti is controlled by a patcher environment and once it is programmed it operates as a stand alone unit. For more information, visit the Axoloti Website, watch the video below and and fund Axoloti.

Update: Good news everyone! Axoloti has been funded!

  • Axoloty Party!

    Axoloty Party!

  • Axoloti Board

    Axoloti Board

  • Axoloti Logo

    Axoloti Logo

  • Axoloti Patch

    Axoloti Patch

~ TarsosLSH in a Photomosaic Web App

TarsosLSH is a Java library implementing Locality-sensitive Hashing (LSH), a practical nearest neighbor search algorithm for high dimensional vectors that operates in sublinear time. The open source software package is authored by me and is available on GitHub: TarsosLSH on GitHub.

With TarsosLSH, Joseph Hwang and Nicholas Kwon from Rice University created an Image Mosaic web application. The application chops an uploaded photo into small blocks. For each block, a color histogram is created and compared with an index of color histograms of reference images. Subsequently each block is replaced with one of the top three nearest neighbors, creating a mosaic. Since high dimensional nearest neighbor search is needed, this is an ideal application for TarsosLSH. The application somewhat proves that TarsosLSH can be used in practical applications, which is comforting.

  • The Starry Night, by Van Ghogh - Original

    The Starry Night, by Van Ghogh - Original

  • The Starry Night, by Van Ghogh in Mosaic as created by the mosaic webapplication.

    The Starry Night, by Van Ghogh in Mosaic as created by the mosaic webapplication.

~ Using the Advantech USB-4716 Data Acquisition Module on a Linux System

Below some notes on installing and using the drivers for the Avandtech USB-4716 on Linux can be found. Since I was unable to find these instructions elsewhere and it took me some time to figure things out, it is perhaps of use to someone else. A similar approach should work for the following devices as well: pci1715, pci1724, pci1734, pci1752, pci1758, pcigpdc, usb4711a, usb4750, pci1711, pci1716, pci1727, pci1747, pci1753_mic3753_pcm3753i, pci1761_pcm3761i, pcm3810i, usb4716, usb4761, pci1714_pcie1744, pci1721, pci1730_pcm3730i, pci1750, pci1756, pci1762, usb4702_usb4704, usb4718

Download the linux driver for the Avandtech USB-4716 DAQ. If you are on a system that can install either deb or rpm use the driver_package. Unzip the package. The driver is split into two parts. A base driver biokernbase and a driver specific for the USB-4716 device, bio4716. The drivers are Linux kernel modules that need to installed. First the base driver needs to be installed, the order is important. After the base driver install the device specific deb kernel module. After a reboot or perhaps immediately this should be the result of executing lsmod | grep bio:

bio4716              23724  0 
biokernbase       17983  1 bio4716
usbcore              128741  9 ehci_hcd,uhci_hcd,usbhid,usb_storage,snd_usbmidi_lib,snd_usb_audio,biokernbase,bio4716

A library to interface with the hardware is provided as a deb package as well. Install this library on your system.

Next download the the examples for the Avandtech USB-4716 DAQ. With the kernel modules installed the system is ready to test the examples in the provided examples directory. If you are using the Java code, make sure to set the java.library.path correctly.

  • Signals acquired using the DAQ

    Signals acquired using the DAQ

~ Audio Fingerprinting - Opportunities for digital musicology

The 27th of November, 2014 a lecture on audio fingerprinting and its applications for digital musicology will be given at IPEM. The lecture introduces audio fingerprinting, explains an audio fingerprinting technique and then goes on to explain how such algorithm offers opportunities for large scale digital musicological applications. Here you can download the slides about audio fingerprinting and its opportunities for digital musicology.

With the explained audio fingerprinting technique a specific form of very reliable musical structure analysis can be done. Below, in the figure section, an example of repetitive structure in the song Ribs Out is shown. Another example is comparing edits or versions of songs. Below, also in the figure section, the radio edit of Daft Punk’s Get Lucky is compared with the original version. Audio synchronization using fingerprinting is another application that is actively used in the field of digital musicology to align audio with extracted features.

Since acoustic fingerprinting makes structure analysis very efficiently it can be applied on a large scale (20k songs). The figure below shows that identical repetition is something that has been used more and more since the mid 1970’s. The trend probably aligns with the amount of technical knowledge needed to ‘copy and paste’ a snippet of music.

How much identical repetition is used in music, over the years

Fig: How much identical repetition is used in music, over the years.

The Panako audio fingerprinting system was used to generate data for these case studies. The lecture and this post are partly inspired by a blog post by Paul Brossier.

  • Radio edit vs. original of Daft Punk's Get Lucky

    Radio edit vs. original of Daft Punk's Get Lucky

  • Spectral peak Acoustic fingerprinting system

    Spectral peak Acoustic fingerprinting system

  • Structure in Ribs Out

    Structure in Ribs Out

  • How much identical repetition is used in a set of 20k songs.

    How much identical repetition is used in a set of 20k songs.

~ ISMIR 2014 - Panako - A Scalable Acoustic Fingerprinting System Handling Time-Scale and Pitch Modification

Panako poster At ISMIR 2014 i will present a paper on a fingerprinting system. ISMIR is the annual conference of the International Society for Music Information Retrieval is the world’s leading interdisciplinary forum on accessing, analyzing, and organizing digital music of all sorts. This years instalment takes place in Taipei, Taiwan. My contribution is a paper titled Panako – A Scalable Acoustic Fingerprinting System Handling Time-Scale and Pitch Modification, it will be presented during a poster session the 27th of October.

This paper presents a scalable granular acoustic fingerprinting system. An acoustic fingerprinting system uses condensed representation of audio signals, acoustic fingerprints, to identify short audio fragments in large audio databases. A robust fingerprinting system generates similar fingerprints for perceptually similar audio signals. The system presented here is designed to handle time-scale and pitch modifications. The open source implementation of the system is called Panako and is evaluated on commodity hardware using a freely available reference database with fingerprints of over 30,000 songs. The results show that the system responds quickly and reliably on queries, while handling time-scale and pitch modifications of up to ten percent.

The system is also shown to handle GSM-compression, several audio effects and band-pass filtering. After a query, the system returns the start time in the reference audio and how much the query has been pitch-shifted or time-stretched with respect to the reference audio. The design of the system that offers this combination of features is the main contribution of this paper.

The system is available, together with documentation and information on how to reproduce the results from the ISMIR paper, on the Panako website. Also available for download is the Panako poster, Panako ISMIR paper and the Panako poster.

  • Results after time scale modification

    Results after time scale modification

  • Results after time stretching

    Results after time stretching

  • Fingerprint and modifications

    Fingerprint and modifications

  • General fingerprinter

    General fingerprinter

  • Results after pitch shifting

    Results after pitch shifting

~ TarsosDSP PureData or MAX MSP external

Pitch detection pure data patch It makes sense to connect TarsosDSP, a real-time audio processing library written in Java, with patcher environments such as Pure Data and Max/MSP. Both Pure Data and Max/MSP offer the capability to code object, or externals using Java. In Pure Data this is done using the pdj~ object, which should be compatible with the Max/MSP implementation. This post demonstrates a patch that connects an oscillator with a pitch tracking algorithm implemented in TarsosDSP.

To the left you can see the finished patch. When it is working an audio stream is generated using an oscillator. The frequency of the oscillator can be controlled. Subsequently the stream is send to the Java environment with the pdj bridge. The Java environment receives an array of floats, representing the audio. A pitch estimation algorithm tries to find the pitch of the audio represented by the buffer. The detected pitch is returned to the pd environment by means of outlet. In pd, the detected pitch is shown and used for auditory feedback.

PitchDetectionResult result = yin.getPitch(audioBuffer);
pitch = result.getPitch();
outlet(0, Atom.newAtom(pitch));

Please note that the pitch detection algorithm can handle any audio stream, not only pure sines. The example here demonstrates the most straightforward case. Using this method all algorithms implemented in TarsosDSP can be used in Pure Data. These range from onset detection to filtering, from audio effects to wavelet compression. For a list of features, please see the TarsosDSP github page. Here, the source for this patch implementing pitch tracking in pd can be downloaded.

~ TarsosDSP on Android - Audio Processing in Java on Android

Audio on AndroidThis post explains how to get TarsosDSP running on Android. TarsosDSP is a Java library for audio processing. Its aim is to provide an easy-to-use interface to practical music processing algorithms implemented, as simply as possible, in pure Java and without any other external dependencies.

Since version 2.0 there are no more references to javax.sound.* in the TarsosDSP core codebase. This makes it easy to run TarsosDSP on Android. Audio Input/Output operations that depend on either the JVM or Dalvik runtime have been abstracted and removed from the core. For each runtime target a Jar file is provided in the TarsosDSP release directory.

The source code for the audio I/O on the JVM and the audio I/O on Android can be found on GitHub. To get an audio processing algorithm working on Android the only thing that is needed is to place TarsosDSP-Android-2.0.jar in the lib directory of your project.

The following example connects an AudioDispatcher to the microphone of an Android device. Subsequently, a real-time pitch detection algorithm is added to the processing chain. The detected pitch in Hertz is printed on a TextView element, if no pitch is present in the incoming sound, -1 is printed. To test the application download and install the TarsosDSPAndroid.apk application on your Android device. The source code is available as well.

AudioDispatcher dispatcher = AudioDispatcherFactory.fromDefaultMicrophone(22050,1024,0);

PitchDetectionHandler pdh = new PitchDetectionHandler() {
        public void handlePitch(PitchDetectionResult result,AudioEvent e) {
                final float pitchInHz = result.getPitch();
                runOnUiThread(new Runnable() {
                    public void run() {
                        TextView text = (TextView) findViewById(R.id.textView1);
                        text.setText("" + pitchInHz);
AudioProcessor p = new PitchProcessor(PitchEstimationAlgorithm.FFT_YIN, 22050, 1024, pdh);
new Thread(dispatcher,"Audio Dispatcher").start();

Thanks to these changes, the fork of TarsosDSP kindly provided by GitHub user srubin, created for a programming assignment at UC Berkley, is not needed any more.

Have fun hacking audio on Android!

~ Haar Wavlet Transform in TarsosDSP

The TarsosDSP Java library for audio processing now contains an implementation of the Haar Wavelet Transform. A discrete wavelet transform based on the Haar wavelet (depicted at the right). This reversible transform has some interesting properties and is practical in signal compression and for analyzing sudden transitions in a file. It can e.g. be used to detect edges in an image.

As an example use case of the Haar transform, a simple lossy audio compression algorithm is implemented in TarsosDSP. It compresses audio by dividing audio into bloks of 32 samples, transforming them using the Haar wavelet Transform and subsequently removing samples with the least difference between them. The last step is to reverse the transform and play the audio. The amount of compressed samples can be chosen between 0 (no compression) and 31 (no signal left). This crude lossy audio compression technique can save at least a tenth of samples without any noticeable effect. A way to store the audio and read it from disk is included as well.

The algorithm works in real time and an example application has been implemented which operates on an mp3 stream. To make this work immediately, the avconv tool needs to be on your system’s path. Also implemented is a bit depth compressor, which shows the effect of (extreme) bit depth compression.

The example is available at the TarsosDSP release directory, the code can be found on the TarsosDSP github page.

  • Haar Wavelet Audio Compression

    Haar Wavelet Audio Compression

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