The DSP library of Tarsos, aptly named TarsosDSP, now contains an implementation of the Goertzel Algorithm. It is implemented using pure Java.
The Goertzel algorithm can be used to detect if one or more predefined frequencies are present in a signal and it does this very efficiently. One of the classic applications of the Goertzel algorithm is decoding the tones generated on by touch tone telephones. These use DTMF-signaling.
Playing music instruments can bring a lot of joy and satisfaction, but not all apsects of music practice are always enjoyable. In this contribution we are addressing two such sometimes unwelcome aspects: the solitude of practicing and the “dumbness” of instruments.
The process of practicing and mastering of music instruments often takes place behind closed doors. A student of piano spends most of her time alone with the piano. Sounds of her playing get lost, and she can’t always get feedback from friends, teachers, or, most importantly, random Internet users. Analysing her practicing sessions is also not easy. The technical possibility to record herself and put the recordings online is there, but the needed effort is relatively high, and so one does it only occasionally, if at all.
Instruments themselves usually do not exhibit any signs of intelligence. They are practically mechanic devices, even when implemented digitally. Usually they react only to direct actions of a player, and the player is solely responsible for the music coming out of the insturment and its quality. There is no middle ground between passive listening to music recordings and active music making for someone who is alone with an instrument.
We have built a prototype of a system that strives to offer a practical solution to the above problems for digital pianos. From ground up, we have built a system which is capable of transmitting MIDI data from a MIDI instrument to a web service and back, exposing it in real-time to the world and optionally enriching it.
A previous post about PeachNote Piano has more technical details together with a video showing the core functionality (quasi-instantaneous USB-BlueTooth-MIDI communication). Some photos can be found below.
While working on a Latex document with several collaborators some problems arise:
Who has the latest version of the TeX-files?
Which LaTeX distributions are in use (MiKTeX, LiveTex,…)
Are all LaTeX packages correctly installed on each computer?
Why is the bibliography, generated with BiBTeX, not included or incomplete?
How does the final PDF look like when it is build by one of the collaborators, with a different LaTeX distribution?
Especially installing and maintaining LaTeX distributions on different platforms (Mac OS X, Linux, Windows) in combination with a lot of LaTeX packages can be challenging. This blog post presents a way to deal with these problems.
Solution
The solution proposed here uses a build-server. The server is responsible for compiling the LaTeX source files and creating a PDF-file when the source files are modified. The source files should be available on the server should be in sync with the latest versions of the collaborators. Also the new PDF-file should be distributed. The syncing and distribution of files is done using a Dropbox install. Each author installs a Dropbox share (available on all platforms) which is also installed on the server. When an author modifies a file, this change is propagated to the server, which, in turn, builds a PDF and sends the resulting file back. This has the following advantages:
Everyone always has the latest version of files;
Only one LaTeX install needs to be maintained (on the server);
The PDF is the same for each collaborator;
You can modify files on every platform with Dropbox support (Linux, Mac OS X, Windows) and even smartphones;
Compiling a large LaTeX file can be computationally intensive, a good task for a potentially beefy server.
Implementation
The implementation of this is done with a couple of bash-scripts running on Ubuntu Linux. LaTeX compilation is handeled by the LiveTeX distribution. The first script compile.bash handles compilation in multiple stages: the cross referencing and BiBTeX bibliography need a couple of runs to get everything right.
#!/bin/bash#first iteration: generate aux file
pdflatex -interaction=nonstopmode --src-specials article.tex
#run bibtex on the aux file
bibtex article.aux
#second iteration: include bibliography
pdflatex -interaction=nonstopmode --src-specials article.tex
#third iteration: fix references
pdflatex -interaction=nonstopmode --src-specials article.tex
#remove unused files
rm article.aux article.bbl article.blg article.out
The second script watcher.bash is more interesting. It watches the Dropbox directory for changes (only in .tex-files) using the efficient inotify library. If a modification is detected the compile script (above) is executed.
#!/bin/bash
directory=/home/user/Dropbox/article/
#recursivly watch te directorywhile inotifywait -r $directory; do#find all files changed the last minute that match tex#if there are matches then do something...if find $directory -mmin -1 | grep tex; then#tex files changed => recompile
echo "Tex file changed... compiling"/bin/bash $directory/compile.bash
#sleep a minute to prevent recompilation loop
sleep 60
fi
done
To summarize: a user-friendly way of collaboration on LaTeX documents was presented. Some server side configuration needs to be done but the clients only need Dropbox and a simple text editor and can start working togheter.
The Pidato experiment demonstrates a rather straightforward method to handle vibrato on a digital piano. It solves the age-old problem on what to do with the enigmatic “vibrato” instructions on some piano solo scores of Franz Liszt. The figure on the right is an exerpt of sonetto 104 del Petrarca.
Since there is no way to perform vibrato on an analogue piano there are all kinds of different interpretations. Interpretations of the ‘vibrato’ instruction include: vibrating the pedal, vibrating the key, simply ignoring it, a vibrato like wiggling with a psychological sounding effect, … A pianist specialized in 19th century music, explains his embodied use of vibrato in a youtube video: Brian Ganz on piano vibrato. Those solutions all seem a bit halfhearted, so I created an alternative approach which resulted in the Pidato experiment.
Pidato is a portmanteau of piano and vibrato, the d, a and o hint to the use of an Arduino. Pidato is also Indonesian for speech, expression. To get a feel of what it actually does I created the video below. Please note that this is a technical demonstration, not an artistic performance… in any way.
Vid: The Pidato experiment – Vibrato on a Digital Piano using an Arduino.
The way it works is by translating movement (accelerometer data) to MIDI messages. The hardware consists of an Arduino, MIDI-ports and a three axis accelerometer. The MIDI-ports are provided by this MIDI IN & OUT Arduino shield. The accelerometer is a MMA7260Q from Sparkfun. Attaching the MMA7260Q and the arduino is done by following the instructions here. One change was made: by attaching the 3.3V output to AREF and executing analogReference(EXTERNAL); fluctuations in power supply cease to have an influence on accelerometer data readings. It is represented by the purple wire in the diagram below.
The software should know when a vibrato like movement is made and how to translate such movement to MIDI messages. The software therefore contains a periodicity estimator and frequency detector to detect how periodic a movement is and how fast the movement is repeated. This was done with the YIN algorithm (more commonly used in audio signal analysis). A periodicity threshold was determined experimentally so the system does not yield false positives when playing the piano in the usual way. Another interesting bit of code is the interrupt setup that samples the accelerometer at a fixed sample rate and sends MIDI messages, also at a fixed rate.
MIDI messaging is done over a serial connection. From the Arduino sending a MIDI message is as simple as calling Serial.print with the correct data. For the task at hand (sending vibrato) Pitch Bend messages were used. The standard Arduino UNO firmware is replaced with Arduino MIDI firmware. This makes the Arduino appear as a standard MIDI device when connected to a computer, which makes interfacing with it practical.
The YIN algorithm is encapsulated in a reusable Arduino library and can be used to detect periodicity and frequency for any signal. This guy used his implementation to create a chromatic tuner. The source code for both the Yin Arduino library and Pidato experiment can be found on github or here.
The Pidato experiment was done with the help the friendly hackers at Hackerspace Ghent.
Tarsos can be used to render MIDI files to audio (WAV) files using arbitrary tone scales. This functionallity can be used to (automatically) verify tone scale extraction from audio files. Since I could not find a dataset with audio and corresponding tone scales creating one using MIDI seemed a good idea.
MIDI files can be found in spades (for example on piano-midi.de or kunstderfuge.com), tone scales on the other hand are harder to find. Luckily there is one massive source, the Scala Tone Scale Archive: A large collection of over 3700 tone scales.
Using Scala tone scale files and a midi files a Tone Scale – Audio dataset can be generated. The quality of the audio depends on the (software) synthesizer and the SoundFont used. Tarsos currently uses the Gervill synthesizer. Gervill is a pure Java software synthesizer with support for 24bit SoundFonts and the MIDI tuning standard.
How To Render MIDI Using Arbitrary Tone Scales with Tarsos
A recent version of the JRE needs to be installed on your system if you want to use Tarsos. Tarsos itself can be downloaded in the form of the MIDI and Scala to Wav – JAR Package.
To test the program you can use a MIDI file and a Scala file and drag and drop those on the graphical interface.
The result should sound like this:
To summarize: by rendering audio with MIDI and Scala tone scale files a dataset with tone scale – audio information can be generated and tone scale extraction algorithms can be tested on the fly.
This is about PeachNote Piano, a project only tangentially related to Tarsos. PeachNote Piano aims to capture as many piano practice sessions as possible and offer useful services using this data. The system does this by capturing and redirecting MIDI events on a Bluetooth enabled smartphone. It is done together with Vladimir Viro and builds on the existing PeachNote infrastructure.
The schema – right – shows the components of the PeachNote Piano system. At the bottom you have a MIDI keyboard connected to the MIDI-Bluetooth-bridge. A smartphone (middle left) receives these MIDI events via Bluetooth and controls the communication to the server (top left). An alternative path goes through a standard computer (top right).
The Arduino based Bluetooth to MIDI bridge is an improvement on the work by Peter Brinkmann. The video below shows communication between USB-MIDI, Bluetooth MIDI and MIDI IN/OUT ports.
As an example application of the PeachNote Piano system we implemented a “Continue a Melody” service which works as follows: a user plays something on a keyboard, maybe just a few notes, and pauses for a few seconds. In the meantime, the server searches through a large database of MIDI piano recordings, finds the longest fuzzy match for the user’s most recent input, and, after a short silence on the users part, starts streaming the continuation of the best matched performance from the database to the user. This mechanism, in fact, is way of browsing a music collection. Users may play a known leitmotiv or just improvise something, and the system continues playing a high quality recording, “replying” to the musical proposition of the user.
More technical details
The melody matching is done on the server, which is implemented in Javascript in the Node.js framework. The whole dataset (about 350 hours of piano recordings) resides in memory in two representations: as a sequence of pitches, and as a sequence of “densities” at the corresponding places of the pitch sequence dataset. This second array is used to store the rough tempo information (number of notes per second) absent in the pitch sequence data.
By combining the two search criteria we can achieve reasonable approximation of the tempo-aware search without its computational complexity.
The implementation of the hardware is based on the open-source electronic prototyping platform Arduino. Optocoupled MIDI ports (IN/OUT) and the BlueSMiRF Bluetooth module were attached to the main board, as can be seen in the middle left block of the schema. The BlueTooth module is configured to use the Serial Port Profile (SPP) which emulates RS-232. The software on the Arduino manages bi-directional, low latency message passing between three serial ports: USB (through an FTDI chip), BlueTooth and the hardware MIDI-IN and OUT port.
The standard Arduino firmware has been replaced with firmware that implements the “Universal Serial Bus Device Class Definition for MIDI Devices”: when attached to a computer via USB, the Arduino shows up as a standard MIDI device, which makes it compatible with all available MIDI software. The software client currently works on the Android smartphone platform. It is represented using the middle right block in the schema. The client can send and receive MIDI events over its Bluetooth port. Pairing, connecting and communicating with the device is done using the Amarino software library. The client communicates with the Peachnote Piano server using TCP sockets implemented on the Dalvik Java runtime.
This article describes how to do makam recognition with a script that uses the Tarsos API.
The task we want to do is to find the tone scales most similar to the one used in recorded music. To complete this task you need a small set of theoretical scales and a large set of music, each brought in one of the scales. To make it more concrete, an example of Turkish classical music is used.
In an article by Bozkurt pitch histograms are used for – amongst other tasks – makam recognition. A maqam defines rules for a composition or performance of classical Turkish music. It specifies melodic shapes and pitch intervals, the scale. The task is to identify which of nine makams is used in a specific song. A simplified, generalized implementation of this task is shown here. In our implementation there is no tonic detection step. Also here we use only theoretical descriptions of the tone scales as a template and do not construct a template using the audio itself, as is done by Bozkurt. Ioannidis Leonidas wrote an interesting master thesis about makam recognition. Since no knowledge of the music itself is used the approach is generally applicable.
The following is an implementation in Scala a general purpose programming language that is interoperable with Jave . The first step is to write the Scala header. This is just some boilerplate code to be able to run the script from the command line – it assumes a UNIX-like environment and tarsos.jar in the same directory:
The second step constructs the templates the capability of Tarsos to create
theoretical tone scale templates using Gaussian kernels is used, line 8. See the attached images for some examples.
val makams = List( "hicaz","huseyni","huzzam","kurdili_hicazar",
"nihavend","rast","saba","segah","ussak")
var theoreticKDEs = Map[java.lang.String,KernelDensityEstimate]()
makams.foreach{ makam =>
val scalaFile = makam + ".scl"
val scalaObject = new ScalaFile(scalaFile);
val kde = HistogramFactory.createPichClassKDE(scalaObject,35)
kde.normalize
theoreticKDEs = theoreticKDEs + (makam -> kde)
}
The third and last step is matching. First a list of audio
files is created by recursively iterating a directory and matching each file to
a regular expression. Next, starting from line 4, each audio file is processed.
The internal implementation of the YIN pitch detection
algorithm is used on the audio file and a pitch class histogram is created
(line 6,7). On line 10 normalization of the histogram is done, to
make the correlation calculation meaningful. Line 11 until 15 compare the
created histogram from the audio file with the templates calculated beforehand.
The results are stored, ordered and eventually printed on line 19.
val directory = "/home/joren/turkish_makams/"
val audio_pattern = ".*.(mp3|wav|ogg|flac)"
val audioFiles = FileUtils.glob(directory,audio_pattern,true).toList
audioFiles.foreach{ file =>
val audioFile = new AudioFile(file)
val detectorYin = PitchDetectionMode.TARSOS_YIN.getPitchDetector(audioFile)
val annotations = detectorYin.executePitchDetection()
val actualKDE = HistogramFactory.createPichClassKDE(annotations,15);
actualKDE.normalize
var resultList = List[Tuple2[java.lang.String,Double]]()
for ((name, theoreticKDE) <- theoreticKDEs){
val shift = actualKDE.shiftForOptimalCorrelation(theoreticKDE)
val currentCorrelation = actualKDE.correlation(theoreticKDE,shift)
resultList = (name -> currentCorrelation) :: resultList
}
//order by correlation
resultList = resultList.sortBy{_._2}.reverse
Console.println(file + " is brought in tone scale " + resultList(0)._1)
}
The DSP library of Tarsos, aptly named TarsosDSP, now contains an implementation of the Goertzel Algorithm. It is implemented using pure Java.
HoGent, Code, Java, and TarsosDSP
goertzel_DTMF_java_7.png, GoertzelDTMF_src.zip, and GoertzelDTMF.jar
The extended abstract about PeachNote Piano has been accepted as a demonstration presentation to appear at the 
While working on a Latex document with several collaborators some problems arise:
The Pidato experiment demonstrates a rather straightforward method to handle vibrato on a digital piano. It solves the age-old problem on what to do with the enigmatic “vibrato” instructions on some piano solo scores of Franz Liszt. The figure on the right is an exerpt of 
