~ The Pidato Experiment: Vibrato on a Digital Piano Using an Arduino

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.

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.

This piano vibrato hack was also covered by hackaday.com and posted to the Hackerspace Ghent blog.

HoGent

Pidato.src.zip, The_Pidato_experiment-Vibrato_on_a_Digital_Piano_using_an_Arduino.webp, vibrato_ff.png, accelerometer_wire_piano_vibrato.jpg, and The_Pidato_experiment-Vibrato_on_a_Digital_Piano_using_an_Arduino.mp4

~ 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.

HoGent

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

~ 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.

Presentation

~ 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:

  






  
#!/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.

  












  
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)
}

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).

HoGent

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

~ Tarsos at 'ISMIR 2011'

A paper about Tarsos was submitted for review at the 12th International Society for Music Information Retrieval Conference which will be held in Miami. The paper Tarsos – a Platform to Explore Pitch Scales in Non-Western and Western Music was reviewed and accepted, it will be published in this year’s proceedings of the ISMIR conference. It can be read below as well.

An oral presentation about Tarsos is going to take place Tuesday, the 25 of October during the afternoon, as can be seen on the ISMIR preliminary program schedule.

If you want to cite our work, please use the following data:

  











  
@inproceedings{six2011tarsos,
  author     = {Joren Six and Olmo Cornelis},
  title      = {Tarsos - a Platform to Explore Pitch Scales 
                in Non-Western and Western Music},
  booktitle  = {Proceedings of the 12th International 
                Society for Music Information Retrieval Conference,
                ISMIR 2011},
  year       = {2011},
  publisher  = {International Society for Music Information Retrieval}
}

Research papers

tarsos_ismir_paper.bibtex.txt and tarsos_ismir_2011.pdf

~ 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.

HoGent

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

~ 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

HoGent

flute_resynth.wav

~ Tarsos at 'IPEM Open House'

The 25th of May 2011 Tarsos was present at the IPEM open house.

IPEM (Institute for Psychoacoustics and Electronic Music) is the research center of the Department of Musicology, which is part of the Department of Art, Music and Theater Studies of Ghent University. IPEM provides a scientific basis for the cultural and creative sector, especially for music and performance arts, and does pioneering research work on the relationship between music body movement and new technologies. The institute consists of an interdisciplinary team but also welcomes visiting researchers from all over the world. One of its aims is also to actively try and validate research results during public events and by means of user studies.

There are close relations between the Royal Conservatory Ghent, where we are located, and IPEM. There is more information about the IPEM open house available. Also available is the program of the IPEM open house 2011

Tarsos was presented using a poster, a flyer and a live demo. The poster about Tarsos and the flyer about Tarsos are both downloadable.

Presentation

tarsos_poster.pdf, ipem_logo.gif, and tarsos_flyer.pdf

~ 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:

  






  
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.

HoGent

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

~ Tarsos at 'First International Workhop of Folk Music Analysis'

Tarsos will be presented at the First International Workhop of Folk Music Analysis: Symbolic and Signal Processing:

“The First International Workhop of Folk Music Analysis: Symbolic and Signal Processing, will take place in Athens, Greece, on the 19th and 20th of May, 2011. … The purpose of the event is to gather reseachers who work in the area of computational folk music analysis, using symbolic or singal processing methods, to present their work, discuss and exchange views on the topic.”

The submitted abstract about Tarsos can be downloaded. A presentation about Tarsos is also available.

HoGent

Tarsos_FMA_2011.pdf and 2011-05-20-tarsos_at_athens.pdf