~ 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 (Command Line Interface) demo applications. The demos should however give a taste of the possibilities. All demo applications follow this pattern:

```ruby\ 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.

```ruby\ java -jar tarsos.jar —help\ java -jar tarsos.jar subcommand —help\ ```

Detect Pitch

```ruby\ java -jar tarsos.jar detect_pitch —in flute.novib.mf.C5B5.wav\ ```

Midi to Audio Using a Scala Tone Scale

```ruby\ java -jar tarsos.jar midi_to_wav —midi satie_gymno1.mid —scala 120.scl\ ```

Audio to Scala Tone Scale

```ruby\ java -jar tarsos.jar audio_to_scala —in out.wav\ ```

Annotate a File

```ruby\ java -jar tarsos.jar annotate —in out.wav\ ```

Pitch table

```ruby\ java -jar tarsos.jar pitch_table\ ```

Music Information Retrieval, Java, Tarsos, and HoGent

tarsos.jar, flute.novib.mf.C5B5.wav, satie_gymno1.mid, and 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.

```java\ 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”:[spectrogram.jar] and execute:

```ruby\ java -jar spectrogram.jar\ ```

Computational ethnomusicology, Code, Java, Music Information Retrieval, Tarsos, and HoGent

spectrum.png and spectrogram.jar

~ 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, Tarsos, and HoGent

~ Dataset

The dataset we use is the sound archive of the department of Ethnomusicology of the Royal Museum for Central Africa at Tervuren, Belgium. The archive was digitized during the DEKKMMA (Digitization of the Ethnomusicological Sound Archive of the Royal Museum for Central Africa - it works better in Dutch) project. More information about the dataset can be foun on the website of the DEKKMMA project:

The archive is a collection of sound recordings of traditional music from Central Africa, with a particular focus on Congo and Rwanda. The sound archive contains about 3,000 hours of music recordings, the oldest of which date from 1910: Edison cylinders recorded by Hutereau in the Uele-province in Congo. The archive contains several sound carriers (Edison cylinders, Sonofil wire, magnetic tapes, audiocassettes, disks, CD's ...) with associated metadata (paper files) and contextual data (photographs, films, video's, books, documents of all kind). The collection was created during and after the colonial era of the Belgian Kingdom in Central Africa. The RMCA collection forms for an important part the musical memory of Central Africa and in terms of size, documentation and musical quality, it is -- without any doubt -- the world's most important sound archive for this region.

Using the meta data we did a rough geocoding of each recording to create an “interactive map of the dataset”:[dataset_geocodes.html].

Computational ethnomusicology and HoGent

dataset_geocodes.html

~ Development and Application of MIR Techniques on Ethnic Music

About

The aim of this research project is to gain novel musicological insights into a large dataset of music from Central Africa. While practising ethnomusicological research on this dataset, we to develop and publish useful software and methodologies for the (ethno)musicological research community.

From November 2009 until November 2013 this research project was organised at the School of Arts, University College Ghent, under supervision by Olmo Cornelis. Later, from November 2013 onwards, the project turned into a 2 year doctoral research project hosted at IPEM, University Ghent under the supervision of Marc Leman.

Partners

• University Ghent, IPEM: The Institute for Psychoacoustics and Electronic Music is part of the Music department at the Faculty of Arts and Philosophy, at Ghent University.

• University College Ghent, School of Arts: The School of Arts, Ghent consists of the Royal Conservatory and KASK.

• Royal Museum for Central Africa, Tervuren: A federal research institute which provides us with the dataset generated during the DEKKMMA (Digitization of the Ethnomusicological Sound Archive of the Royal Museum for Central Africa - the abbreviation works better in Dutch.) project.

Computational ethnomusicology, Music Information Retrieval, HoGent, and UGent