The TarsosDSP Java library for audio processing now contains a module for spectral peak extraction. It calculates a short time Fourier transform and subsequently finds the frequency bins with most energy present using a median filter. The frequency estimation for each identified bin is significantly improved by taking phase information into account. A method described in “Sethares et al. 2009 – Spectral Tools for Dynamic Tonality and Audio Morphing”.
The noise floor, determined by the median filter, the spectral information itself and the estimated peak locations are returned for each FFT-frame. Below a visualization of a flute can be found. As expected, the peaks are harmonically spread over the complete spectrum up until the Nyquist frequency.
Spectral peaks of a flute. The first 10 harmonic are detected up until the Nyquist frequency.
Give students an intensive course in the most advanced and current topics in the research fields of systematic musicology and sound and music computing. Give students the opportunity to discuss their research proposals/project with an international staff of teachers representing a variety of expertise in different domains of systematic musicology and sound and music computing. Teach students the most recent knowledge and basic skills needed to start a PhD. Give students the opportunity to join the research communities on systematic musicology, on sound and music computing.
Next to the lectures, the informal meetings with the professors was very interesting. I got to add some things to my ‘to read’ list:
Rolf Bader, Calculation of Helmholtz frequency of a Renaissance vihuela string instrument with five tone hole
Schneider, A. & Frieler, K. (2009) Perception of harmonic and inharmonic sounds: Results from
ear models. In S. Ystad, R. Kronland-Martinet & K. Jensen (Eds.), Computer music modeling and retrieval. Genesis of meaning in sound and music (pp. 18–44). Berlin: Springer.
This post contains some info on how do some basic home automation: it shows how cheap remote controlled power sockets can be managed using a computer. The aim is to power on or power off lights, a stereo or other devices remotely from a command shell.
The solution here uses an Arduino connected to a 433.33MHz transmitter. Via a Ruby script installed on the computer a command is send over serial to the Arduino. Subsequently the Arduino sends the command over the air to the power socket(s). If all goes well the power socket reacts by switching the connecting device on or off.
In the video below the process is shown. The command line interface controls the light via the Arduino. It should show the general idea.
The following Ruby script simply sends the binary control codes to the Arduino. For this type of power socket the code consist of a five bit group code and five bit device code. The Arduino is connected to /dev/tty.usbmodem411.
The code below is the complete Arduino sketch. It uses the RCSwich library, which makes the implementation very simple. Essentially it waits for a complete command and transmits it through the connected transmitter. The transmitter connected is a tx433n
#include <RCSwitch.h>RCSwitch mySwitch = RCSwitch();
char command[12];//2x5 for device and group + command
int index = 0;
char currentChar = -1;
//the led pin in use
int ledPin = 12;
void setup() {
//start the serial communication
Serial.begin(9600);
//433MHZTransmitter is connected to ArduinoPin#10
mySwitch.enableTransmit(10);
//Led connected to led pin
pinMode(ledPin, OUTPUT);
Serial.println("Started the power command center! Mwoehahaha!");
}
void readCommand(){
//read a command
while (Serial.available() > 0){
if(index < 11){
currentChar = Serial.read(); //Read a character
command[index] = currentChar; //Store it
index++; //Increment where to write next
command[index] = '\0'; // append termination char
}
}
}
void loop() {
//read a command
readCommand();
//if a command is complete
if(index == 11){
Serial.print("Recieved command: ");
Serial.println(command);
char operation = command[0];
char* group = &command[1];
//group is 5 bits, as is device
char* device = &command[6];
//execute the operation
doSwitch(operation,group,device);
//reset the index to read a new command
index=0;
}
}
void doSwitch(char operation, char* group, char* device){
digitalWrite(ledPin, HIGH);
if(operation == '1'){
mySwitch.switchOn(group, device);
Serial.print("Switched on device ");
} else {
mySwitch.switchOff(group, device);
Serial.print("Switched off device ");
}
Serial.println(device);
digitalWrite(ledPin, LOW);
}
Cheap remote controlled power sockets.
Remote and power socket with DIP-switch.
Finished case.
Messy breadboard prototype
The underside of the Arduino shield
Soldered 'Arduino shield' to control power sockets.
Semantic Audio is concerned with content-based management of digital audio recordings. The rapid evolution of digital audio technologies, e.g. audio data compression and streaming, the availability of large audio libraries online and offline, and recent developments in content-based audio retrieval have significantly changed the way digital audio is created, processed, and consumed. New audio content can be produced at lower cost, while also large audio archives at libraries or record labels are opening to the public. Thus the sheer amount of available audio data grows more and more each day. Semantic analysis of audio resulting in high-level metadata descriptors such as musical chords and tempo, or the identification of speakers facilitate content-based management of audio recordings. Aside from audio retrieval and recommendation technologies, the semantics of audio signals are also becoming increasingly important, for instance, in object-based audio coding, as well as intelligent audio editing, and processing. Recent product releases already demonstrate this to a great extent, however, more innovative functionalities relying on semantic audio analysis and management are imminent. These functionalities may utilise, for instance, (informed) audio source separation, speaker segmentation and identification, structural music segmentation, or social and Semantic Web technologies, including ontologies and linked open data.
This conference will give a broad overview of the state of the art and address many of the new scientific disciplines involved in this still-emerging field. Our purpose is to continue fostering this line of interdisciplinary research. This is reflected by the wide variety of invited speakers presenting at the conference.
The paper presents TarsosDSP, a framework for real-time audio analysis and processing. Most libraries and frameworks offer either audio analysis and feature extraction or audio synthesis and processing. TarsosDSP is one of a only a few frameworks that offers both analysis, processing and feature extraction in real-time, a unique feature in the Java ecosystem. The framework contains practical audio processing algorithms, it can be extended easily, and has no external dependencies. Each algorithm is implemented as simple as possible thanks to a straightforward processing pipeline. TarsosDSP’s features include a resampling algorithm, onset detectors, a number of pitch estimation algorithms, a time stretch algorithm, a pitch shifting algorithm, and an algorithm to calculate the Constant-Q. The framework also allows simple audio synthesis, some audio effects, and several filters. The Open Source framework is a valuable contribution to the MIR-Community and ideal fit for interactive MIR-applications on Android. The full paper can be downloaded TarsosDSP, a Real-Time Audio Processing Framework in Java
@inproceedings{six2014tarsosdsp,
author = {JorenSixandOlmoCornelisandMarcLeman},
title = {{TarsosDSP, a Real-TimeAudioProcessingFrameworkinJava}},
booktitle = {{Proceedings of the 53rd AESConference (AES53rd)}},
year = 2014
}
Woensdag 18 december 2013 organiseerde Olmo Cornelis een concert in het kader van zijn doctoraat. De dag erna volgde zijn verdediging. Nogmaals proficiat Olmo met het mooie eeh mbirapunt. Hieronder staat kort wat uitleg over het project en het concert.
In zijn onderzoeksproject ‘Exploring the symbiosis of Western and non-Western Music’ stelde Olmo Cornelis de beschrijving van Centraal-Afrikaanse muziek centraal. Deze werd verkend via computationele technieken die de klank als signaal
benaderden. De verkregen informatie zorgde voor beïnvloeding van het artistieke oeuvre waarin steeds een mengeling van impliciete en expliciete etnische invloeden spelen.
In het kader van de afronding van dit doctoraal onderzoek spelen het HERMESensemble, het Nadar Ensemble, Maja Jantar en Françoise Vanhecke op 18 december werk van Olmo Cornelis dat tijdens dit project geschreven werd. Het onderzoeksproject Exploring the symbiosis of Western and non-Western Music werd in 2008 geïnitieerd aan het Conservatorium / School of Arts van de HoGent en werd gefinancierd door het onderzoeksfonds Hogeschool Gent.
Beeld: Noel Cornelis, Reality of Possibilities, 2012
Yesterday, December 4th 2013, the RTBF was at IPEM to do a small feature on research currently going on at the institue. The RTBF is the public broadcasting organization of the French Community of Belgium, the southern, French-speaking part of Belgium. The clip shows the D-Jogger in action, with me using it. The fragment is available on the RTBF website and is embedded below.
The journal paper Evaluation and Recommendation of Pulse and Tempo Annotation in Ethnic Music – In Journal Of New Music Research by Cornelis, Six, Holzapfel and Leman was published in a special issue about Computational Ethnomusicology of the Journal of New Music Research on the 20th of august 2013. Below you can find the abstract for the article, and the full text author version of the article itself.
Abstract: Large digital archives of ethnic music require automatic tools to provide musical content descriptions. While various automatic approaches are available, they are to a wide extent developed for Western popular music. This paper aims to analyze how automated tempo estimation approaches perform in the context of Central-African music. To this end we collect human beat annotations for a set of musical fragments, and compare them with automatic beat tracking sequences. We first analyze the tempo estimations derived from annotations and beat tracking results. Then we examine an approach, based on mutual agreement between automatic and human annotations, to automate such analysis, which can serve to detect musical fragments with high tempo ambiguity.
@article{cornelis2013tempo_jnmr,
author = {OlmoCornelis, JorenSix, AndreHolzapfel, andMarcLeman},
title = {{EvaluationandRecommendation of Pulse ant TempoAnnotationinEthnicMusic}},
journal = {{Journal of NewMusicResearch}},
volume = {42},
number = {2},
pages = {131-149},
year = {2013},
doi = {10.1080/09298215.2013.812123}
}
The DSP library for Taros, aptly named TarsosDSP, now includes an implementation of a Constant-Q Transform (as of version 1.6). The Constant-Q transform does essentially the same thing as an FFT, but has the advantage that each octave has the same amount of bins. This makes the Constant-Q transform practical for applications processing music. If, for example, 12 bins per octave are chosen, these can correspond with the western musical scale.
Also included in the newest release (version 1.7) is a way to visualize the transform, or other musical features. The visualization implementation is done together with Thomas Stubbe.
The example application below shows the Constant-Q transform with an overlay of pitch estimations. The corresponding waveform is also shown.
The journal paper Tarsos, a Modular Platform for Precise Pitch Analysis of Western and Non-Western Music by Six, Cornelis, and Leman was published in a special issue about Computational Ethnomusicology of the Journal of New Music Research on the 20th of august 2013. Below you can find the abstract for the article, and pointers to audio examples, the Tarsos software, and the author version of the article itself.
Abstract: This paper presents Tarsos, a modular software platform used to extract and analyze pitch organization in music. With Tarsos pitch estimations are generated from an audio signal and those estimations are processed in order to form musicologically meaningful representations. Tarsos aims to offer a flexible system for pitch analysis through the combination of an interactive user interface, several pitch estimation algorithms, filtering options, immediate auditory feedback and data output modalities for every step. To study the most frequently used pitches, a fine-grained histogram that allows up to 1200 values per octave is constructed. This allows Tarsos to analyze deviations in Western music, or to analyze specific tone scales that differ from the 12 tone equal temperament, common in many non-Western musics. Tarsos has a graphical user interface or can be launched using an API – as a batch script. Therefore, it is fit for both the analysis of individual songs and the analysis of large music corpora. The interface allows several visual representations, and can indicate the scale of the piece under analysis. The extracted scale can be used immediately to tune a MIDI keyboard that can be played in the discovered scale. These features make Tarsos an interesting tool that can be used for musicological analysis, teaching and even artistic productions.
Ladrang Kandamanyura (slendro pathet manyura), is the name of the piece used in the article throughout section 2. The album on which the piece can be found is available at wergo. Below a thirty second fragment is embedded. You can also download the thirty second fragment to analyse it yourself.
Below the BibTex entry for the article is embedded.
In the extended abstract, also titled Computer Assisted Transcription of Ethnic Music, it is described how the Tarsos software program now has features aiding transcription. Tarsos is especially practical for ethnic music of which the tone scale is not known beforehand. The proceedings of FMA 2013 are available as well.
During the conference there also was an interesting panel on transcription. The following people participated: John Ashley Burgoyne, moderator (University of Amsterdam), Kofi Agawu (Princeton University), Dániel P. Biró (University of Victoria), Olmo Cornelis (University College Ghent, Belgium), Emilia Gómez (Universitat Pompeu Fabra, Barcelona), and Barbara Titus (Utrecht University). Some pictures can be found below.
TarsosLSH is a Java library implementing Locality-sensitive Hashing (LSH), a practical nearest neighbour search algorithm for multidimensional vectors that operates in sublinear time. It supports several Locality Sensitive Hashing (LSH) families: the Euclidean hash family (L2), city block hash family (L1) and cosine hash family. The library tries to hit the sweet spot between being capable enough to get real tasks done, and compact enough to serve as a demonstration on how LSH works. It relates to the Tarsos project because it is a practical way to search for and compare musical features.
git clone https://JorenSix@github.com/JorenSix/TarsosLSH.git
cd TarsosLSH/build
ant #Builds the core TarsosLSH library
ant javadoc #build the API documentation
When everything runs correctly you should be able to run the command line application, and have the latest version of the TarsosLSH library for inclusion in your projects. Also, the Javadoc documentation for the API should be available in TarsosLSH/doc. Drop me a line if you use TarsosLSH in your project. Always nice to hear how this software is used.
The fastest way to get something on your screen is executing this on your command line: java - jar TarsosLSH.jar this lets LSH run on a random data set. The full reference of the command line application is included below:
Name
TarsosLSH: finds the nearest neighbours in a data set quickly, using LSH.
Synopsis
java - jar TarsosLSH.jar [options] dataset.txt queries.txt
Description
Tries to find nearest neighbours for each vector in the
query file, using Euclidean (L2) distance by default.
Both dataset.txt and queries.txt have a similar format:
an optional identifier for the vector and a list of N
coordinates (which should be doubles).
[Identifier] coord1 coord2 ... coordN
[Identifier] coord1 coord2 ... coordN
For an example data set with two elements and 4 dimensions:
Hans 12 24 18.5 -45.6
Jane 13 19 -12.0 49.8
Options are:
-f cos|l1|l2
Defines the hash family to use:
l1 City block hash family (L1)
l2 Euclidean hash family(L2)
cos Cosine distance hash family
-r radius
Defines the radius in which near neighbours should
be found. Should be a double. By default a reasonable
radius is determined automatically.
-h n_hashes
An integer that determines the number of hashes to
use. By default 4, 32 for the cosine hash family.
-t n_tables
An integer that determines the number of hash tables,
each with n_hashes, to use. By default 4.
-n n_neighbours
Number of neighbours in the neighbourhood, defaults to 3.
-b
Benchmark the settings.
--help
Prints this helpful message.
Examples
Search for nearest neighbours using the l2 hash family with a radius of 500
and utilizing 5 hash tables, each with 3 hashes.
java - jar TarsosLSH.jar -f l2 -r 500 -h 3 -t 5 dataset.txt queries.txt
Source Code Organization
The source tree is divided in three directories:
src contains the source files of the core DSP libraries.
test contains unit tests for some of the DSP functionality.
build contains ANT build files. Either to build Java documentation or runnable JAR-files for the example applications.
Further Reading
This section includes a links to resources used to implement this library.
The LSH-page maintained by Alexandr Andoni contains pointers to good resources:
Locality-Sensitive Hashing Scheme Based on p-Stable Distributions a chapter by Alexandr Andoni, Mayur Datar, Nicole Immorlica, Piotr Indyk, and Vahab Mirrokni which appeared in the book Nearest Neighbor Methods in Learning and Vision: Theory and Practice, by T. Darrell and P. Indyk and G. Shakhnarovich (eds.), MIT Press, 2006.
This post documents how to implement a simple music quiz with the meta data provided by Spotify and a big red button.
During my last birthday party, organized at Hackerspace Ghent ,there was an ongoing Spotify music quiz. The concept was rather simple: If music that was playing was created in the same year as I was born, the guests could press a big red button and win a price! If they guessed incorrectly, they were publicly shamed with a sad trombone. Only one guess for each
song was allowed.
Below you can find a small videograph which shows the whole thing in action. The music quiz is simple: press the button if the song is created in 1984. In the video, at first a wrong answer is given, then a couple of invalid answers follow. Finally a good answer is given, the song “The Killing Moon by Echo & the Bunnymen” is from 1984! Woohoo!
Allright, what did I use to to implement the spotify music quiz:
A big red Arduino button, attatched by USB to a laptop.
A system with Spotify.
A way to access the meta data of the currently playing song in Spotify.
A Ruby script to connect al the parts and checks answers.
The Red Button
A nice big red button, which main features are that it is red and big, serves as the main input device for the quiz. To be able to connect the salvaged safety button to a computer via USB, an Arduino Nano is used. The Arduino is loaded with the code from the debounce tutorial, basically unchanged. Unfortunately, I could not fit the the FTDI-chip, which provides the USB connection, in the original enclosure. An additional enclosure, the white one seen in the pictures below, was added.
When pressed, the button sends a signal over the serial line. See below how the Ruby Quiz script waits for, and handles such event.
Get Meta Data from the Currently Playing Song in Spotify
Another requirement for the quiz to work is the ability to get information about the song currently playing in Spotify. On Linux this can be done with the dbus interface for Spotify. The script included below returns information about the artist, album and title of the song. It also detects if Spotify is running or not. Find it, fork it, use it, on github
#!/usr/bin/python## now_playing.py# # Python script to fetch the meta data of the currently playing# track in Spotify. This is tested on Ubuntu.
import dbus
bus = dbus.SessionBus()
try:
spotify = bus.get_object('com.spotify.qt', '/')
iface = dbus.Interface(spotify, 'org.freedesktop.MediaPlayer2')
meta_data = iface.GetMetadata()
artistname = ",".join(meta_data['xesam:artist'])
trackname = meta_data['xesam:title']
albumname = meta_data['xesam:album']
#Other fields are:# 'xesam:trackNumber', 'xesam:discNumber','mpris:trackid',# 'mpris:length','mpris:artUrl','xesam:autoRating','xesam:contentCreated','xesam:url'
print str(trackname + " | " + artistname + " | " + albumname + " | Unknown")
except dbus.exceptions.DBusException:
print "Spotify is not running."
The Quiz Ruby Script
With the individual part in order, we now need Ruby glue to paste it all together. The complete music quiz script can be found on github. The main loop, below, waits for a button press. If it is pressed the sad trombone, invalid answer or winner sound is played. The sounds are attached to this post.
whiletruedo# Wait for a button press
data = sp.readline
# Fetch meta data about the currently# playing song
result = `#{now_playing_command}`# Parse the meta data
title,artist,album = parse_result(result)
#Title is hash key, should be unique within playlist
key = title
if responded_to.has_key? key
puts "Already answered: you cheater"
play cheater
elsif correct_answers.has_key? key
puts "Correct answers: woohoo"
responded_to[key]=true
play winner
else
puts "Incorrect answer: sad trombone"
responded_to[key]=true
play sad_trombone
endend
The DSP library for Taros, aptly named TarsosDSP, now includes an example demonstrating the flanging audio effect. Flanging, essentialy mixing the signal with a varying delay of itself, produces an interesting interference pattern.
The flanging example works on wav-files or on input from microphone. Try it yourself, download Flanging.jar, the executable jar file. Below you can check what flanging sounds like with various parameters.
The source code of the Java implementation can be found on the TarsosDSP github page.
The DSP library for Taros, aptly named TarsosDSP, now includes an example showing how to synthesize cat sounds. The inspration came from this youtube video
To hear what exactly it does, listen to the following audio example.
There is also a command line interface, the following command does
The DSP library for Taros, aptly named TarsosDSP, now includes an example showing how to synthesize pitch estimations. The goal of the example is to show which errors are made by different pitch detectors.
To test the application, download and execute the Resynthesizer.jar file and load an audio file. For the moment only 44.1kHz mono wav is allowed. To hear what exactly it does, compare the following two audio fragments:
There is also a command line interface, the following command does pitch tracking, and follows the envelope of in.wav and immediately plays it on the default audio device. If you want to save the audio, see the command line options. The flute example is provided for your convenience.
java -jar Resynthesizer-latest.jar in.wav
_______ _____ _____ _____
|__ __| | __ \ / ____| __ \
| | __ _ _ __ ___ ___ ___| | | | (___ | |__) |
| |/ _` | '__/ __|/ _ \/ __| | | |\___ \| ___/
| | (_| | | \__ \ (_) \__ \ |__| |____) | |
|_|\__,_|_| |___/\___/|___/_____/|_____/|_|
----------------------------------------------------
Name:
TarsosDSP resynthesizer
----------------------------------------------------
Synopsis:
java -jar CommandLineResynthesizer.jar [--detector DETECTOR] [--output out.wav] [--combined combined.wav] input.wav
----------------------------------------------------
Description:
Extracts pitch and loudnes from audio and resynthesises the audio with that information.
The result is either played back our written in an output file.
There is als an option to combine source and synthezized material
in the left and right channels of a stereo audio file.
input.wav a readable wav file.
--output out.wav a writable file.
--combined combined.wav a writable output file. One channel original, other synthesized.
--detector DETECTOR defaults to FFT_YIN or one of these:
YIN
MPM
FFT_YIN
DYNAMIC_WAVELET
AMDF
The source code of the Java implementation of the synthesizer can be found on the TarsosDSP github page.
The DSP library for Taros, aptly named TarsosDSP, now includes an implementation of a pitch shifting algorithm (as of version 1.4) and a time stretching algorithm. Combined, the two can be used for something like phase vocoding. With a phase vocoder you can load an audio snippet, change the pitch and duration and e.g. create a library of snippets. E.g. by recording one piano key stroke, it is possible to generate two octaves of samples of different lengths, and use those in stead of synthesized samples. The following example application shows exactly that, implemented in the java programming language.
The example application below shows how to pitch shift and time stretch a sample to create a sample library with the TarsosDSP library.
Today marks the reslease of Tarsos 1.0 . The new Tarsos release contains practical transcription features. As can be seen in the screenshot below, a time stretching feature makes it easy to loop a certain audio fragment while it is playing in a slow tempo. The next loop can be played with by pressing the n key, the one before by pressing b.
Since the pitch classes can be found in a song, and there is a feature that lets you play a MIDI keyboard in the tone scale of the song under analysis, transcription of ethnic music is made a lot easier.
The new release of Tarsos can be found in the Tarsos release repository. From now on, nightly releases are uploaded there automatically.
The DSP library for Taros, aptly named TarsosDSP, now includes an implementation of a pitch shifting algorithm (as of version 1.4). The goal of pitch shifting is to change the pitch of a piece of audio without affecting the duration. The algorithm implemented is a combination of resampling and time stretching. Resampling changes the pitch of the audio, but affects the total duration. Consecutively, the duration of the audio is stretched to the original (without affecting pitch) with time stretching. The result is very similar to phase vocoding.
The example application below shows how to pitch shift input from the microphone in real-time, or pitch shift a recorded track with the TarsosDSP library.
To test the application, download and execute the PitchShift.jar file and load an audio file. For the moment only 44.1kHz mono wav is allowed. To get started you can try this piece of audio.
There is also a command line interface, the following command lowers the pitch of in.wav by two semitones.
java -jar in.wav out.wav -200
----------------------------------------------------
_______ _____ _____ _____
|__ __| | __ \ / ____| __ \
| | __ _ _ __ ___ ___ ___| | | | (___ | |__) |
| |/ _` | '__/ __|/ _ \/ __| | | |\___ \| ___/
| | (_| | | \__ \ (_) \__ \ |__| |____) | |
|_|\__,_|_| |___/\___/|___/_____/|_____/|_|
----------------------------------------------------
Name:
TarsosDSP Pitch shifting utility.
----------------------------------------------------
Synopsis:
java -jar PitchShift.jar source.wav target.wav cents
----------------------------------------------------
Description:
Change the play back speed of audio without changing the pitch.
source.wav A readable, mono wav file.
target.wav Target location for the pitch shifted file.
cents Pitch shifting in cents: 100 means one semitone up,
-100 one down, 0 is no change. 1200 is one octave up.
The resampling feature was implemented with libresample4j by Laszlo Systems. libresample4j is a Java port of Dominic Mazzoni’s libresample 0.1.3, which is in turn based on Julius Smith’s Resample 1.7 library.
The 13th International Society for Music Information Retrieval Conference took place in Porto, Portugal, October 8th-12th, 2012. This text contains links to some papers, toolkits, software presented there which are interesting for my research. Basically it contains my personal highlights of the conference. The ISMIR 2012 is described as follows:
The annual Conference of the International Society for Music Information Retrieval (ISMIR) is the world’s leading research forum on processing, searching, organizing and accessing music-related data. The revolution in music distribution and storage brought about by digital technology has fueled tremendous research activities and interests in academia as well as in industry. The ISMIR Conference reflects this rapid development by providing a meeting place for the discussion of MIR-related research, developments, methods, tools and experimental results. Its main goal is to foster multidisciplinary exchange by bringing together researchers and developers, educators and librarians, as well as students and professional users.
Tutorials
I saw an interesting tutorial on Jazz music and a tutorial on source separation. After an introduction, which detailed the experimental basis of the system, a source separator was introduced. The REPET source separator is a relatively simple system that yields reasonable results to split accompaniment from foreground melody.
The ongoing work by Ceril Bohak and Matija Marolt on segmentation of folk music could be very useful to apply on Afican musics. The paper is called Finding Repeating Stanzas in Folk Songs.
At this years ICMC Conference, ICMC 2012 we presented a paper describing a way to experiment with tone scales and how to use Tarsos as a compositional tool. What follows are some pointers to the presentation, paper and to other interesting talks that were presented there.
ICMC 2012 was organized in Ljubljana from the 9 to 14 septembre and had a very dense program of talks, posters, presentations, demos and concerts.
Since 1974 the International Computer Music Conference has been the major international forum for the presentation of the full range of outcomes from technical and musical research, both musical and theoretical, related to the use of computers in music. This annual conference regularly travels the globe, with recent conferences in the Americas, Europe and Asia. This year we welcome the conference to Slovenia for the first time.
Sound to Scale to Sound, a Setup for Microtonal Exploration and Composition
@inproceedings{cornelis2012sound_to_scale,
author = {OlmoCornelisandJorenSix},
title = {{Sound to Scale to Sound, a SetupforMicrotonalExplorationandComposition}},
booktitle = {{Proceedings of the 2012InternationalComputerMusicConference,
(ICMC2012)}},
year = {2012},
publisher = {TheInternationalComputerMusicAssociation}
}
Program highlights
What follows are a number of pointers to my personal program highlights.
Verena Thomas presented two very well polished software tools. One to detect patterns in scores, called motifviewer and a tool to search in score databases in a multi-modal way. The Probado tool does score-to-audio alignment and much more.
Gibber is an impressive live-coding environment with an easy syntax. Since it is all done with javascript you can start playing with it immediately. Overtone Another live-coding environment, presented at the conference by Sam Aaron, was equally impressive. It is programmed using the Closure language.
At ICMC there were a number of tools to assist in composition. One of those is The Bach Project, by Andrea Agostini. Togheter with CatART by Diemo Swartz it forms a very expressive platform to work with sound, which was demonstrated by Aaron Einbond and Christopher Trapani in their paper titled Precise Pitch Control In Real Time Corpus-Based Concatenative Synthesis. Diemo Swartz presented work on Audio Mosaicing, it can be seen as a follow-up to AuidioGuild by Ben Hackbarth.
I also got to know the work by Thomas Grill, on his website a nice piece of software can be found a Python implementation of the Non Stationary Gabor Transform. Another software system I got to know is the functional signal processing programming language FAUST
My personal highlights of the concert programme include the works by Johannes Kreidler, Aura Pon, Daniel Mayer, Alexander Schubert and the remarkable performance by Dexter Ford. The concept behind Soundlog by Johannes Kretz was also interesting.
UGent
aes53_tarsos_dsp.pdf
The journal paper Evaluation and Recommendation of Pulse and Tempo Annotation in Ethnic Music – In Journal Of New Music Research by Cornelis, Six, Holzapfel and Leman was published in a special issue about Computational Ethnomusicology of 
The journal paper Tarsos, a Modular Platform for Precise Pitch Analysis of Western and Non-Western Music by Six, Cornelis, and Leman was published in a special issue about Computational Ethnomusicology of 
The 13th International Society for Music Information Retrieval Conference took place in Porto, Portugal, October 8th-12th, 2012. This text contains links to some papers, toolkits, software presented there which are interesting for my research. Basically it contains my personal highlights of the conference. The 
At this years 