~ TarsosDSP used in jAM - Java Automatic Music Transcription

TarsosDSP, a small Java DSP library, has been used in a bachelor thesis: Entwicklung eines Systems zur automatischen Notentranskription von monophonischem Audiomaterial by Michael Wager.

The goal of the thesis was to develop an automatic transcription system for monophonic music. You can download the latest version of jAM – Java Automatic Music Transcription.

If you want to use TarsosDSP, please consult the TarsosDSP page on github or read more about TarsosDSP here.

Java en TarsosDSP

~ Kinderuniversiteit - Muziek onder de microscoop!

Zondag 18 december 2011 gaf ik een workshop voor de Gentse kinderuniversiteit. Het thema van de kinderuniversiteit was Muziek onder de microscoop. De teaser voor de workshop is hier te vinden:

WORKSHOP – Muziek (ont)luisteren op de computer
Is het mogelijk om piano te spelen op een tafel? Kan een computer luisteren naar muziek en er van genieten? Wat is muziek eigenlijk, en hoe werkt geluid?
Tijdens deze workshop worden de voorgaande vragen beantwoord met enkele computerprogramma’s!

Concreet worden enkele componenten van geluid (en bij uitbreiding, muziek) gedemonstreerd met computerprogrammaatjes gemaakt in het conservatorium:

• Geluidssterkte: een decibel-meter met een bepaalde drempelwaarde. Probeer zo luid mogelijk te doen en zie hoe moeilijk het is om, eens een bepaald niveau bereikt is, in decibel te stijgen.
• Toonhoogte: een klein spelletje om toonhoogte aan te tonen. Probeer zo juist mogelijk te zingen of te fluiten en vergelijk je score.
• Percussie: dit programma reageert op handgeklap. Hoe kan je het onderscheid maken tussen bijvoorbeeld een fluittoon en handgeklap?

De foto’s hieronder geven een sfeerbeeld.

Presentation, Dutch, Java, en TarsosDSP

logo-with-arrow.png, UtterAsterisk.jar, PercussionDetector.jar, PitchDetector.jar, en SoundDetector.jar

~ Dual-Tone Multi-Frequency (DTMF) Decoding with the Goertzel Algorithm in Java

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.

To make the algorithm visually appealing a Java Swing interface has been created(visible right). You can try this application by running the Goertzel DTMF Jar-file. The souce code is included in the jar and is avaliable as a separate zip file. The TarsosDSP github page also contains the source for the Goertzel algorithm Java implementation.

DTMF detection of 9

DTMF detection of 2

Code, Java, en TarsosDSP

goertzel_DTMF_java_7.png, GoertzelDTMF.jar, en GoertzelDTMF_src.zip

~ Rendering MIDI Using Arbitrary Tone Scales - Revisited

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.

Drag and drop MIDI and scala files

Create WAV files

Code, Java, en Computational musicology

MidiToWav.jar, MIDI_file.mid, en persian.scl.txt

~ PeachNote Piano

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.

Finished enclosure

Building a Bluetooth - MIDI shield

Assembled

PeachNote Piano in action

PeachNote Schema

Code, Java, Presentation, en PeachNote Piano

~ Makam Recognition with the Tarsos API

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:

  
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#!/bin/sh
exec scala  -cp tarsos.jar -savecompiled "$0" "$@"
!#
import be.hogent.tarsos.util._
//other import statements

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.

  
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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.

  
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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)
}

A complete version of this script can is available: Tone scale matching script Results of the script when ran on Bozkurt’s dataset can be seen in the attached spreadsheet (openoffice format or excel format).

Theoretical template

Other theoretical template

Actual Hicaz song overlayed with a theoretical template

Computational ethnomusicology, Code, Java, Music Information Retrieval, en Tarsos

guess_makam.scala, makams.zip, makam_recognition_results.ods, en makam_recognition_results.xls

~ Latex export functions

Tarsos, a software package to analyse pitch organization in music, contains a new output modality. It is now possible to export a pitch class histogram and a pitch class interval matrix to latex from within Tarsos. This makes documenting tone scales more efficient.

An example for a pitch class histogram and pitch class interval matrix can be seen. Also available is the latex source code.

Latex export of a pitch class histo.

Latex export of a pitch class interval matrix

Computational ethnomusicology, Java, en Tarsos

latex_export.tex, latex_export.pdf, en MR.1954.1.19-4.dat

~ Resynthesis of Pitch Detection Annotations on a Flute Piece

Tarsos, a software package to analyse pitch organization in music, contains a new output modality. Now it is possible to export resynthesized pitch annotations, detected by a pitch detection algorithm and compare those with the original sound. This can be interesting to see which errors a pitch detection algorithm makes.

Below you can listen to an example of synthesized pitch detection results compared with the original flute piece. The file starts with only the original flute sound (on the right channel) and gradually changes so only the synthesized annotations (on the left channel) can be heard.

Resynthesis of Pitch Detection Annotations on a Flute Piece by Joren Six

Computational ethnomusicology, Music Information Retrieval, Code, Java, en Tarsos

flute_resynth.wav

~ PulseAudio Support for Sun Java 6 on Ubuntu

This article describes how to make sun-java6 play nice with the PulseAudio sound sytem on Ubuntu with an x64 processor architecture. With some changes the method should also work with other operating systems and other platforms.

The default way sun-java6 operates with respect to sound on Ubuntu is, well unrespectfull. When playing audio it claims an audio device, which then can not be used any more by other applications trying to access the same device. This is far from ideal. Also changing audio interfaces (by e.g. plugging in a USB audio interface) goes wrong most of the time.

These problems are addressed by PulseAudio and there is a way to make sun-java6 aware of PulseAudio on Ubuntu. The OpenJDK does this automatically but it has some other, unrelated, issues. If you want to use PulseAudio with java6 on Ubuntu x64 you need copy pulse-java.jar and platform dependent libpulse-java.so file to correct JVM directories. To make it easy you can execute these commands:

  
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wget http://tarsos.0110.be/attachment/cons/255/libpulse-java.so
sudo cp libpulse-java.so /usr/lib/jvm/java-6-sun/jre/lib/amd64

wget http://tarsos.0110.be/attachment/cons/256/pulse-java.jar
sudo cp pulse-java.jar /usr/lib/jvm/java-6-sun/jre/lib/ext

From this moment on the “PulseAudio Mixer” is available for Java applications. Sharing, switching and assigning audio devices to Java programs is as a result smooth. To use the PulseAudio Mixer by default you need to change sound.properties which can be found at /usr/lib/jvm/java-6-sun/jre/lib/sound.properties. Details can be found here.

Code en Java

libpulse-java.so, pulse-java.jar, en pulseaudio_logo.png

~ TarsosDSP: a small JAVA audio processing library

TarsosDSP is a collection of classes to do simple audio processing. It features an implementation of a percussion onset detector and two pitch detection algorithms: Yin and the Mcleod Pitch method.

Its aim is to provide a simple interface to some audio (signal) processing algorithms implemented in JAVA.

To make some of the possibilities clear I coded some examples.

• TarsosDSP UtterAsterisk a game that shows real-time pitch detection with YIN.
• TarsosDSP Sound Detector is simply to show how to react when (loud) sound is available.
• TarsosDSP Percussion Detector is capable of detecting percussion onsets using the method described here. It plays a sound from freesound.org when percussion is detected.

The source code of TarsosDSP is available on github.

Presentation at Newline

Saturday the 25th of March TarsosDSP was presented at Newline, a small conference organized by whitespace. Here you can download the slides I used to present TarsosDSP, I also created an introductory text on sound and Java.

Percussion detection

UtterAsterisk

Sound Detector

Code, Java, featured, TarsosDSP, en Tarsos

PitchDetector.jar, PercussionDetector.jar, tarsosDSP_presentation.pdf, sound_and_java.pdf, UtterAsterisk.jar, en SoundDetector.jar

~ Groovy Tarsos Scripting

There is more to Tarsos then meets te eye. The graphical user interface only exposes some functionality; the API exposes all of Tarsos’ capabilities.

Tarsos is programmed in Java so the API is accessible trough Java and other programming languages targeting the JVM like JRuby, Scala and Groovy. The following examples use the Groovy programming language because I find it the most aesthetically pleasing with regards to interoperability and it gets the job done without getting in your way.

To run the examples a copy of the Tarsos JAR-file needs to be added to the Classpath and the Groovy runtime must be installed correctly. I’ll leave this as an exercise for the reader: godspeed to you, brave soul. Quick protip: placing a copy of the jar in the extensions directory seems to work best, e.g. see important java directories on mac OS X.

The first example extracts pitch class histograms from a bunch of files and saves them as EPS-files. It iterates a directory recursively and handles each file that matches a given regular expression. In this example the regular expression matches all WAV-files. Batch processing is one of those things scripting is ideal for, doing the same thing with the user interface would be tedious or even mind-numbingly boring, not groovy at all indeed.

  
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import be.hogent.tarsos.*
import be.hogent.tarsos.util.*
import be.hogent.tarsos.util.histogram.ToneScaleHistogram
import be.hogent.tarsos.sampled.pitch.Annotation
import be.hogent.tarsos.sampled.pitch.PitchDetectionMode

dir = "/home/joren/audio"

FileUtils.glob(dir,".*.wav",true).each { file ->
        audioFile = new AudioFile(file)
        pitchDetector = PitchDetectionMode.TARSOS_YIN.getPitchDetector(audioFile)
        pitchDetector.executePitchDetection()
        //get some annotations
        annotations = pitchDetector.getAnnotations()
        //create an ambitus and tone scale histogram
        ambitusHistogram = Annotation.ambitusHistogram(annotations)
        toneScaleHisto = ambitusHistogram.toneScaleHistogram()
        //plot a smoothed version of the histogram
        p = new SimplePlot()
        p.addData 0, toneScaleHisto.gaussianSmooth(0.2)
        p.save FileUtils.basename( file) + ".eps"
}

The second example uses functionality that is currently only available trough the API. It takes a MIDI-file and synthesizes it to a wave file using an arbitrary scale. In this case 10-TET. The heavy-work is done by the Gervill synthesizer. The resulting file is available for download, micro—macro?—tonal Bach is great: BWV 1013 in 10-TET. The result of an analysis with Tarsos on the synthesized audio clearly shows an interval of 120 cents with some deviations.

  
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import java.io.File
import be.hogent.tarsos.midi.MidiToWavRenderer
import be.hogent.tarsos.util.ScalaFile

midiFile = new File("BWV_1013.mid")
outFile = new File("out.wav")

tuning = [0,120,240,360,480,600,720,840,960,1080] as double []

MidiToWavRenderer renderer
renderer = new MidiToWavRenderer()
renderer.setTuning(tuning)
renderer.createWavFile(midiFile, outFile)

BWV 1013 10-TET by jorensix

An extended version of this second example script could be used to generate a dataset with audio and corresponding tone scale information on the fly. The dataset could then be used as a baseline.

The API is not yet well documented and is still in flux or more correctly: superflux. Note to self: I will provide documentation and a number of useful examples when the dust settles down. I’m not even sure if I will stick with Groovy. Scala has a nice Lispy feel to it and seems more developed. Groovy has a less steep learning curve, especially if you have some experience with Ruby. JRuby is also nice but the interoperability with legacy Java looks like an ugly hack.

EPS export as PNG

10-TET in Tarsos

Code, Java, featured, en Tarsos

medium.png, 120.png, en BWV_1013_10-TET.mp3

~ Tarsos User Interface Prototype

Tarsos now has an easy to use drag and drop User Interface. It can be used to extract tone scale information from audio files.

Start tarsos using JAVA WebStart.

Drag and drop works for scala tone scale files and different kinds of audio files. Audiofiles are transcoded automagically using an embedded ffmpeg binary which is platform dependend. It works on linux and windows, on other platforms only WAV files are supported.

Some of the current features:

• Scala file extraction from audio
• Real time pitch tracking
• Real time pitch class histogram visualization
• Alignment of pitch intervals with histogram using mouse dragging
• MIDI Tuning messages based on scala files.
• Histogram smoothing functions
• … a lot more is available under the hood.

Analysis

Browser

Computational ethnomusicology, Java, featured, en Tarsos

~ Reproduction of speech using MIDI

Tarsos is now capable of reproducing speech using MIDI. The idea to convert speech into MIDI comes from the blog of Corban Brook where the following video can be found:

Another example of music inspired by speech is this interview with Louis Van Gaal:

Tarsos sends out midi data based on an FFT analysis of the signal. It maps the spectrogram to MIDI Messages and uses the power spectrum to calculate the velocity of each note on message.

The implementation can run in real-time but the output has some delay: the FFT calculation, constructing MIDI messages, calculating velocity, synthesizing sound, … is not instantaneous.

To use this capability Tarsos supports the following syntax. If a MIDI file is given the MIDI messages are written to the file. If an audio file is given Tarsos uses the audio as input. If the --pitch switch is used only the F0 is considered to construct MIDI messages instead of a complete FFT.

  


  
java -jar tarsos.jar pitch_to_midi [--pitch] [midi_out.midi] [audio_in.wav]

Java

ttm.mp3, ttm.midi, sitting_in_a_room.mp3, sitting_in_a_room.midi, en tarsos.jar

~ Static Code Analysis For Java Using Eclipse

This post is about the tools I use to keep the source code of Tarsos reasonably clean, consistent and readable. Static code analysis can be of great help if you want to maintain strict coding standards and follow language idioms. Some of the patterns they can detect for you:

• Dead code – unused variables, parameters, methods
• Suboptimal code – wasteful resource usage
• Overcomplicated expressions – unnecessary if statements, for loops that could be while loops
• Duplicate code – copied/pasted code is a code smell.
• Formatting inconsistencies, e.g. variable modifier order

And even more subtle, but equally important:

• Resource management: is a resource handled (closed) correctly on all possible code paths?
• Abstraction level: is it needed to expose the concrete type of an object or could an (abstract) supertype or even an interface be used instead?
• …

In a previous life I used .NET and the static code analysis tools FxCop & StyleCop. FxCop operates on bytecode (or intermediate language in .NET parlance) level, StyleCop analyses the source code itself. Tarsos uses JAVA so I looked for JAVA alternatives and found a few.

• PMD & Checkstyle both operate on source code level.
• FindBugs operates on bytecode level.

On freesoftwaremagazine.com there is an article series on JAVA static code analysis software. It covers PMD and FixBugs and integration in Eclipse. It does not cover Checkstyle. Checkstyle is essentialy the same as PMD but it is better integrated in eclipse: it checks code on save and uses the standard ‘Problems’ interface, PMD does not.

To fix problems Eclipse save actions can save you some time. IBM has an article on how to keep your code clean using Eclipse.

Continuous testing is also a really nice thing to have: detecting unexpected behavior while refactoring/programming can prevent unnecessary bug hunts. A video about immediate feedback using continuous testing makes this clear.

Another tip is a more philosophical one: making your code and code revisions publicly available makes you think twice before implementing (and subsequently publishing) a quick and dirty hack. Tarsos is available on github.

References

• FxCop & StyleCop
• Checkstyle
• Find bugs
• PDM
• Infinitest
• Github

PMD

Checkstyle

Java, Code, en featured

~ Tarsos demos

I just finished creating a first release of Tarsos. The release contains several demo applications, some more usefull than other. Tarsos is a work in progress: not all functionality is exposed with the CLI demo applications. The demos should however give a taste of the possibilities. All demo applications follow this pattern:

  


  
java -jar tarsos.jar subcommand [--option [argument] ...]

To get help the --help switch can be used. It generates contextual help for either the subcommand or for Tarsos itself.

  
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java -jar tarsos.jar --help
java -jar tarsos.jar subcommand --help

Detect Pitch

  


  
java -jar tarsos.jar detect_pitch --in flute.novib.mf.C5B5.wav

Midi to Audio Using a Scala Tone Scale

  


  
java -jar tarsos.jar midi_to_wav --midi satie_gymno1.mid --scala 120.scl

Audio to Scala Tone Scale

  


  
java -jar tarsos.jar audio_to_scala --in out.wav

Annotate a File

  


  
java -jar tarsos.jar annotate --in out.wav

Pitch table

  


  
java -jar tarsos.jar pitch_table

Music Information Retrieval, Java, en Tarsos

tarsos.jar, flute.novib.mf.C5B5.wav, satie_gymno1.mid, en 120.scl.txt

~ Tarsos Spectrogram

Today I created a spectrogram application using Tarsos. The application listens to an audio input, computes an FFT and at the same time calculates pitch. The expected pitch is overlaid on the spectrogram. All this happens real-time and is implemented using JAVA.

This is the most recent version of the spectrogram implementation in java.

  
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float pitch = Yin.processBuffer(buffer, (float) sampleRate);
fft.transform(buffer);
double maxAmplitude = 0;
for (int j = 0; j < buffer.length / 2; j++) {
        double amplitude = buffer[j] * buffer[j] + buffer[j + 
                buffer.length/2] * buffer[j+ buffer.length/2];
        amplitude = Math.pow(amplitude, 0.5);
        colorIndexes[j] = amplitude;
        maxAmplitude = Math.max(amplitude, maxAmplitude);
}

If you want to test it yourself download the spectrogram jar package and execute:

  


  
java -jar spectrogram.jar

Computational ethnomusicology, Code, Java, Music Information Retrieval, en Tarsos

spectrum.png en spectrogram.jar

~ YIN Pitch Tracker in JAVA

To make Tarsos more portable I wrote a pitch tracker in pure JAVA using the YIN algorithm based on the implementation in C of aubio. The implementation also uses some code written by Karl Helgasson and Teun de Lange.

It can be used to perform real time pitch detection or to analyse files. To use it as a real time pitch detector just start the JAR-file by double clicking. To analyse a file execute one of the following. The first results in a list of annotations (text), the second shows the annotations graphically.

  
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java -jar pitch_detector_yin.jar  flute.novib.mf.C5B5.wav
java -jar pitch_detector_yin.jar  --file flute.novib.mf.C5B5.wav

The provided flute sample is from The Musical Samples library of the University of Iowa and converted to mono wav. The source code of the pitch tracker can be found below.

Screenshot

Research papers, featured, Music Information Retrieval, Code, Java, en Tarsos

flute.novib.mf.C5B5.wav, pitch_detector_yin.jar, en pitch_detector_yin_src.zip

~ Tarsos on GitHub

The JAVA software program we are developing is called Tarsos and can now be found on GitHub. GitHub is a web-based hosting service for projects that use the Git version control system.

Currently Tarsos is a collection of Java classes to create, compare and process pitch-frequency data using histograms. In it’s current state it is not usable for end-users.

Credits

Tarsos is developed at University College Ghent, Faculty of Music and uses a number of open source libraries:

• Gervill: a software sound synthesizer, supports the MIDI Tuning Standard. API.
• Jave: a wrapper for ffmpeg.
• Apache Commons Math: a library of lightweight, self-contained mathematics and statistics components API.
• JASS: a unit generator based audio synthesis programming environment. API.
• Java-getopt: a port of the GNU getopt family of functions. API.
• Ptplot a 2D plotting library. API.

Computational ethnomusicology, featured, Code, Java, en Tarsos