I am currently in Birmingham, UK at the 2019 at the joint Analytical Approaches to World Music (AAWM) and Folk Music Conference. The opening concert by the RBC folk ensemble already provided the most lively and enthusiastic conference opening probably ever. Especially considering the early morning hour (9.30). At the conference, two studies will be presented on which I collaborated:
Automatic comparison of human music, speech, and bird song suggests uniqueness of human scales
The uniqueness of human music relative to speech and animal song has been extensively debated, but rarely directly measured. We applied an automated scale analysis algorithm to a sample of 86 recordings of human music, human speech, and bird songs from around the world. We found that human music throughout the world uniquely emphasized scales with small-integer frequency ratios, particularly a perfect 5th (3:2 ratio), while human speech and bird song showed no clear evidence of consistent scale-like tunings. We speculate that the uniquely human tendency toward scales with small-integer ratios may relate to the evolution of synchronized group performance among humans.
Automatic comparison of global children’s and adult songs
Music throughout the world varies greatly, yet some musical features like scale structure display striking crosscultural similarities. Are there musical laws or biological constraints that underlie this diversity? The “vocal mistuning” hypothesis proposes that cross-cultural regularities in musical scales arise from imprecision in vocal tuning, while the integer-ratio hypothesis proposes that they arise from perceptual principles based on psychoacoustic consonance. In order to test these hypotheses, we conducted automatic comparative analysis of 100 children’s and adult songs from throughout the world. We found that children’s songs tend to have narrower melodic range, fewer scale degrees, and less precise intonation than adult songs, consistent with motor limitations due to their earlier developmental stage. On the other hand, adult and children’s songs share some common tuning intervals at small-integer ratios, particularly the perfect 5th (~3:2 ratio). These results suggest that some widespread aspects of musical scales may be caused by motor constraints, but also suggest that perceptual preferences for simple integer ratios might contribute to cross-cultural regularities in scale structure. We propose a “sensorimotor hypothesis” to unify these competing theories.
Thanks to the support of a travel grant by the faculty of Arts and Philosophy of Ghent University I was able to attend the ISMIR 2018 conference. A conference on Music Information Retrieval. I am co author on a contribution for the the Late-Breaking / Demos session
The structure of musical scales has been proposed to reflect universal acoustic principles based on simple integer ratios. However, some studying tuning in small samples of non-Western cultures have argued that such ratios are not universal but specific to Western music. To address this debate, we applied an algorithm that could automatically analyze and cross-culturally compare scale tunings to a global sample of 50 music recordings, including both instrumental and vocal pieces. Although we found great cross-cultural diversity in most scale degrees, these preliminary results also suggest a strong tendency to include the simplest possible integer ratio within the octave (perfect fifth, 3:2 ratio, ~700 cents) in both Western and non-Western cultures. This suggests that cultural diversity in musical scales is not without limit, but is constrained by universal psycho-acoustic principles that may shed light on the evolution of human music.
Claims made in many Music Information Retrieval (MIR) publications are hard to verify due to the fact that (i) often only a textual description is made available and code remains unpublished – leaving many implementation issues uncovered; (ii) copyrights on music limit the sharing of datasets; and (iii) incentives to put effort into reproducible research – publishing and documenting code and specifics on data – is lacking. In this article the problems around reproducibility are illustrated by replicating an MIR work. The system and evaluation described in ‘A Highly Robust Audio Fingerprinting System’ is replicated as closely as possible. The replication is done with several goals in mind: to describe difficulties in replicating the work and subsequently reflect on guidelines around reproducible research. Added contributions are the verification of the reported work, a publicly available implementation and an evaluation method that is reproducible.
This weekend the University Hamburg – Institute for Systematic Musicology and more specifically Christian D. Koehn organized the International Symposium on Computational Ethnomusicological Archiving. The symposium featured a broad selection of research topics (physical modelling of instruments, MIR research, 3D scanning techniques, technology for (re)spacialisation of music, library sciences) which all had a relation with archiving musics of the world:
How could existing digital technologies in the field of music information retrieval, artificial intelligence, and data networking be efficiently implemented with regard to digital music archives? How might current and future developments in these fields benefit researchers in ethnomusicology? How can analytical data about musical sound and descriptive data about musical culture be more comprehensively integrated?
In this presentation we describe our experience of working with computational analysis on digitized wax cylinder recordings. The audio quality of these recordings is limited which poses challenges for standard MIR tools. Unclear recording and playback speeds further hinder some types of audio analysis. Moreover, due to a lack of systematical meta-data notation it is often uncertain where a single recording originates or when exactly it was recorded. However, being the oldest available sound recordings, they are invaluable witnesses of various musical practices and they are opportunities to improve the understanding of these practices. Next to sketching these general concerns, we present results of the analysis of pitch content of 400 wax cylinder recordings from Indiana University (USA) and from the Royal Museum from Central Africa (Belgium). The scales of the 400 recordings are mapped and analyzed as a set. It is found that the fifth is almost always present and that scales with four and five pitch classes are organized similarly and differ from those with six and seven pitch classes, latter center around intervals of 170 cents, and former around 240 cents.
I have contributed to the 4th International Digital Libraries for Musicology workshop (DLfM 2017) which was organized in Shanghai, China. It was a satellite event of the ISMIR 2017 conference. Unfortunately I did not mange to find funding to attend the workshop, I did however contribute as co-author to two proceeding papers. Both were presented by Reinier de Valk (thanks again).
This study is a call for action for the music information retrieval (MIR) community to pay more attention to collaboration with digital music archives. The study, which resulted from an interdisciplinary workshop and subsequent discussion, matches the demand for MIR technologies from various archives with what is already supplied by the MIR community. We conclude that the expressed demands can only be served sustainably through closer collaborations. Whereas MIR systems are described in scientific publications, usable implementations are often absent. If there is a runnable system, user documentation is often sparse—-posing a huge hurdle for archivists to employ it. This study sheds light on the current limitations and opportunities of MIR research in the context of music archives by means of examples, and highlights available tools. As a basic guideline for collaboration, we propose to interpret MIR research as part of a value chain. We identify the following benefits of collaboration between MIR researchers and music archives: new perspectives for content access in archives, more diverse evaluation data and methods, and a more application-oriented MIR research workflow.
This work focuses on applications of duplicate detection for managing digital music archives. It aims to make this mature music information retrieval (MIR) technology better known to archivists and provide clear suggestions on how this technology can be used in practice. More specifically applications are discussed to complement meta-data, to link or merge digital music archives, to improve listening experiences and to re-use segmentation data. The IPEM archive, a digitized music archive containing early electronic music, provides a case study.
The active listening demo movie above should explain the aim system succinctly. It shows two different ways to provide ‘context’ to audio playing in the room. In the first instance beats information is used to synchronize smartphones and flash the screen, the second demo shows a tactile feedback device responding to beats. The device is a soundbrenner pulse tactile metronome and was kindly sponsored by the company that sells these.
The article titled “Synchronizing Multimodal Recordings Using Audio-To-Audio Alignment” by Joren Six and Marc Leman has been accepted for publication in the Journal on Multimodal User Interfaces. The article will be published later this year. It describes and tests a method to synchronize data-streams. Below you can find the abstract, pointers to the software under discussion and an author version of the article itself.
Synchronizing Multimodal Recordings Using Audio-To-Audio Alignment An Application of Acoustic Fingerprinting to Facilitate Music Interaction Research
Abstract:Research on the interaction between movement and music often involves analysis of multi-track audio, video streams and sensor data. To facilitate such research a framework is presented here that allows synchronization of multimodal data. A low cost approach is proposed to synchronize streams by embedding ambient audio into each data-stream. This effectively reduces the synchronization problem to audio-to-audio alignment. As a part of the framework a robust, computationally efficient audio-to-audio alignment algorithm is presented for reliable synchronization of embedded audio streams of varying quality. The algorithm uses audio fingerprinting techniques to measure offsets. It also identifies drift and dropped samples, which makes it possible to find a synchronization solution under such circumstances as well. The framework is evaluated with synthetic signals and a case study, showing millisecond accurate synchronization.
The algorithm under discussion is included in Panako an audio fingerprinting system but is also available for download here. The SyncSink application has been packaged separately for ease of use.
To use the application start it with double click the downloaded SyncSink JAR-file. Subsequently add various audio or video files using drag and drop. If the same audio is found in the various media files a time-box plot appears, as in the screenshot below. To add corresponding data-files click one of the boxes on the timeline and choose a data file that is synchronized with the audio. The data-file should be a CSV-file. The separator should be ‘,’ and the first column should contain a time-stamp in fractional seconds. After pressing Sync a new CSV-file is created with the first column containing correctly shifted time stamps. If this is done for multiple files, a synchronized sensor-stream is created. Also, ffmpeg commands to synchronize the media files themselves are printed to the command line.
This work was supported by funding by a Methusalem grant from the Flemish Government, Belgium. Special thanks goes to Ivan Schepers for building the balance boards used in the case study. If you want to cite the article, use the following BiBTeX:
@article{six2015multimodal,
author = {Joren Six and Marc Leman},
title = {{Synchronizing Multimodal Recordings Using Audio-To-Audio Alignment}},
issn = {1783-7677},
volume = {9},
number = {3},
pages = {223-229},
doi = {10.1007/s12193-015-0196-1},
journal = {{Journal of Multimodal User Interfaces}},
publisher = {Springer Berlin Heidelberg},
year = 2015
}
The synchronized data from the two webcams, accelerometer and balanceboard in ELAN. From top to bottom the synchronized streams are two video-streams, balance-board data (red), accelerometer-data (green) and audio (black).
Conceptual drawing used as a basis for the SyncSync application. A reference stream (blue) can be synchronized with streams one and two. It allows a workflow where streams are started and stopped (red) or start before the reference stream (green).
A microcontroller fitted with an electret microphone and a microSD card slot. It can record audio in real-time together with sensor data.
SyncSink Synchronize media files. A user-friendly interface to synchronize media and data files. First a reference media-file is added using drag-and-drop. The audio steam of the reference is extracted and plotted on a timeline as the topmost box. Subsequently other media-files are added. The offsets with respect to the reference are calculated and plotted. CSV-files with timestamps and data recorded in sync with a stream can be attached to a respective audio stream. Finally, after pressing Sync!, the data and media files are modified to be exactly in sync with the reference.
Multimodal recording system diagram. Each webcam has a microphone and is connected to the pc via USB. The dashed arrows represent analog signals. The balance board has four analog sensors but these are simplified to one connection in the schematic. The analog output of the microphones is also recorded through the DAQ. An analog accelerometer is connected with a microcontroller which also records audio.
Two streams of audio with fingerprints marked. Some fingerprints are present in both streams (green, O) while others are not (red, x). Matching fingerprints have the same offset, indicated by the dotted lines.
Synchronized streams in Sonic Visualizer. Here you can see two channel audio synchronized with accelerometer data (top, green) and balanceboard data (bottom, purple).
The 27th of November, 2014 a lecture on audio fingerprinting and its applications for digital musicology will be given at IPEM. The lecture introduces audio fingerprinting, explains an audio fingerprinting technique and then goes on to explain how such algorithm offers opportunities for large scale digital musicological applications. Here you can download the slides about audio fingerprinting and its opportunities for digital musicology.
With the explained audio fingerprinting technique a specific form of very reliable musical structure analysis can be done. Below, in the figure section, an example of repetitive structure in the song Ribs Out is shown. Another example is comparing edits or versions of songs. Below, also in the figure section, the radio edit of Daft Punk’s Get Lucky is compared with the original version. Audio synchronization using fingerprinting is another application that is actively used in the field of digital musicology to align audio with extracted features.
Since acoustic fingerprinting makes structure analysis very efficiently it can be applied on a large scale (20k songs). The figure below shows that identical repetition is something that has been used more and more since the mid 1970’s. The trend probably aligns with the amount of technical knowledge needed to ‘copy and paste’ a snippet of music.
Fig: How much identical repetition is used in music, over the years.
At ISMIR 2014 i will present a paper on a fingerprinting system. ISMIR is the annual conference of the International Society for Music Information Retrieval is the world’s leading interdisciplinary forum on accessing, analyzing, and organizing digital music of all sorts. This years instalment takes place in Taipei, Taiwan. My contribution is a paper titled Panako – A Scalable Acoustic Fingerprinting System Handling Time-Scale and Pitch Modification, it will be presented during a poster session the 27th of October.
This paper presents a scalable granular acoustic fingerprinting system. An acoustic fingerprinting system uses condensed representation of audio signals, acoustic fingerprints, to identify short audio fragments in large audio databases. A robust fingerprinting system generates similar fingerprints for perceptually similar audio signals. The system presented here is designed to handle time-scale and pitch modifications. The open source implementation of the system is called Panako and is evaluated on commodity hardware using a freely available reference database with fingerprints of over 30,000 songs. The results show that the system responds quickly and reliably on queries, while handling time-scale and pitch modifications of up to ten percent.
The system is also shown to handle GSM-compression, several audio effects and band-pass filtering. After a query, the system returns the start time in the reference audio and how much the query has been pitch-shifted or time-stretched with respect to the reference audio. The design of the system that offers this combination of features is the main contribution of this paper.
The system is available, together with documentation and information on how to reproduce the results from the ISMIR paper, on the Panako website. Also available for download is the Panako poster, Panako ISMIR paper and the Panako poster.
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.
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 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.
What follows is about the Conference on Interdisciplinary Musicology and the 15th international Conference of the Gesellschaft fur Musikfoschung. First this text will give information about our contribution to CIM2012: Revealing and Listening to Scales From the Past; Tone Scale Analysis of Archived Central-African Music Using Computational Means and then a number of highlights of the conference follow. The joint conference took place from the 4th to the 8th of september 2012.
In 2012, CIM will tackle the subject of History. Hosted by the University of Göttingen, whose one time music director Johann Nikolaus Forkel is widely regarded as one of the founders of modern music historiography, CIM12 aims to promote collaborations that provoke and explore new methods and methodologies for establishing, evaluating, preserving and communicating knowledge of music and musical practices of past societies and the factors implicated in both the preservation and transformation of such practices over time.
Revealing and Listening to Scales From the Past; Tone Scale Analysis of Archived Central-African Music Using Computational Means
The work presented by Rytis Ambrazevicius et al. Modal changes in traditional Lithuanian singing: Diachronic aspect has a lot in common with our research, it was interesting to see their approach. Another highlight of the conference was the whole session organized by Klaus-Peter Brenner around Mbira music.
Rainer Polak gave a talk titled ‘Swing, Groove and Metre. Asymmetric Feels, Metric Ambiguity and Metric Transformation in African Musics’. He showed how research about rhythm in jazz research, music theory and empirical musicology ( amongst others) could be bridged and applied to ethnic music.
The overview Eleanore Selfridge-Field gave during her talk Between an Analogue Past and a Digital Future: The Evolving Digital Present was refreshing. She had a really clear view on all the different ways musicology and digital media can benifit from each-other.
From the concert programme I found two especially interesting: the lecture-performance by Margarete Maierhofer-Lischka and Frauke Aulbert of Lotofagos, a piece by Beat Furrer and Burdocks composed and performed by Christian Wolff and a bunch of enthusiastic students.
After about a year of development and several revisions TarsosDSP has enough features and is stable enough to slap the 1.0 tag onto it. A ‘read me’, manual, API documentation, source and binaries can be found on the TarsosDSP release directory. The source is present in the
What follows below is the information that can be found in the read me file:
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. Also included is a Goertzel DTMF decoding algorithm and a time stretch algorithm (WSOLA).
Its aim is to provide a simple interface to some audio (signal) processing algorithms implemented in pure JAVA. Some TarsosDSP example applications are available.
The following example filters a band of frequencies of an input file testFile. It keeps the frequencies form startFrequency to stopFrequency.
git clone https://JorenSix@github.com/JorenSix/TarsosDSP.git
cd TarsosDSP/build
ant tarsos_dsp_library #Builds the core TarsosDSP library
ant build_examples #Builds all the TarsosDSP examples
ant javadoc #Creates the documentation in TarsosDSP/doc
When everything runs correctly you should be able to run all example applications and have the latest version of the TarsosDSP library for inclusion in your projects. Also the Javadoc documentation for the API should be available in TarsosDSP/doc. Drop me a line if you use TarsosDSP in your project. Always nice to hear how this software is used.
Source Code Organization and Examples of TarsosDSP
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.
examples contains a couple of example applications with a Java Swing user interface:
SoundDetector show how you loudness calculations can be done. When input sound is over a defined limit an event is fired.
PitchDetector this demo application shows real-time pitch detection. When pitch is detected the hertz value is printed together with a probability.
PercussionDetector show the percussion (onset) dectection. Clapping your hands causes an event. This demo application also shows the influence of the two parameters on the algorithm.
UtterAsterisk a game with the goal to sing as close to a melody a possible. Technically it shows real-time pitch detection with YIN or MPM.
Spectrogram in Java shows a spectrogram and detected pitch, either live or from an audio file. It is interesting to see which frequencies are picked as fundamentals.
Goertzel DTMF decoding an implementation of the Goertzel Algorithm. A fancy user interface shows what goes on under the hood.
Audio Time Stretching – Implementation in Pure Java Using WSOLA an implementation of a time stretching algorithm. WSOLA makes it possible to change the play back speed of audio without changing the pitch. The play back speed can be changed at any moment, even when there is audio playing.
Tarsos contains a couple of useful command line applications. They can be used to execute common tasks on lots of files. Dowload Tarsos and call the applications using the following format:
The first part java -jar tarsos.jar tells the Java Runtime to start the correct application. The first argument for Tarsos defines the command line application to execute. Depending on the command, required arguments and options can follow.
To get a list of available commands, type java -jar tarsos.jar -h. If you want more information about a command type java -jar tarsos.jar command -h
Detect Pitch
Detects pitch for one or more input audio files using a pitch detector. If a directory is given it traverses the directory recursively. It writes CSV data to standard out with five columns. The first is the start of the analyzed window (seconds), the second the estimated pitch, the third the saillence of the pitch. The name of the algorithm follows and the last column shows the original filename.
Friday the second of December I presented a talk about software for music analysis. The aim was to make clear which type of research topics can benefit from measurements by software for music analysis. Different types of digital music representations and examples of software packages were explained.
Following presentation was used during the talk. (ppt, odp):
Sonic Visualizer: As its name suggests Sonic Visualizer contains a lot different visualisations for audio. It can be used for analysis (pitch,beat,chroma,…) with VAMP-plugins. To quote “The aim of Sonic Visualiser is to be the first program you reach for when want to study a musical recording rather than simply listen to it”. It is the swiss army knife of audio analysis.
BeatRoot is designed specifically for one goal: beat tracking. It can be used for e.g. comparing tempi of different performances of the same piece or to track tempo deviation within one piece.
Tartini is capable to do real-time pitch analysis of sound. You can e.g. play into a microphone with a violin and see the harmonics you produce and adapt you playing style based on visual feedback. It also contains a pitch deviation measuring apparatus to analyse vibrato.
Tarsos is software for tone scale analysis. It is useful to extract tone scales from audio. Different tuning systems can be seen, extracted and compared. It also contains the ability to play along with the original song with a tuned midi keyboard .
To show the different digital representations of music one example (Liebestraum 3 by Liszt) was used in different formats:
The aim of acoustic fingerprinting is to generate a small representation of an audio signal that can be used to identify or recognize similar audio samples in a large audio set. A robust fingerprint generates similar fingerprints for perceptually similar audio signals. A piece of music with a bit of noise added should generate an almost identical fingerprint as the original. The use cases for audio fingerprinting or acoustic fingerprinting are myriad: detection of duplicates, identifying songs, recognizing copyrighted material,…
Using a pitch class histogram as a fingerprint seems like a good idea: it is unique for a song and it is reasonably robust to changes of the underlying audio (length, tempo, pitch, noise). The idea has probably been found a couple of times independently, but there is also a reference to it in the literature, by Tzanetakis, 2003: Pitch Histograms in Audio and Symbolic Music Information Retrieval:
Although mainly designed for genre classification it is possible that features derived from Pitch Histograms might also be applicable to the problem of content-based audio identification or audio fingerprinting (for an example of such a system see (Allamanche et al., 2001)). We are planning to explore this possibility in the future.
Unfortunately they never, as far as I know, did explore this possibility, and I also do not know if anybody else did. I found it worthwhile to implement a fingerprinting scheme on top of the Tarsos software foundation. Most elements are already available in the Tarsos API: a way to detect pitch, construct a pitch class histogram, correlate pitch class histograms with a pitch shift,… I created a GUI application which is presented here. It is, probably, the first open source acoustic / audio fingerprinting system based on pitch class histograms.
It works using drag and drop and the idea is to find a needle (an audio file) in a hay stack (a large amount of audio files). For every audio file in the haystack and for the needle pitch is detected using an optimized, for speed, Yin implementation. A pitch class histogram is created for each file, the histogram for the needle is compared with each histogram in the hay stack and, hopefully, the needle is found in the hay stack.
Unfortunately I do not have time for rigorous testing (by building a large acoustic fingerprinting data set, or an other decent test bench) but the idea seems to work. With the following modifications, done with audacity effects the needle was still found a hay stack of 836 files :
A 10% speedup
15 and 30 seconds removed form the needle (a song of 4 minutes 12 seconds)
White noise added
Reversed the audio (This is, I believe, a rather unique property of this fingerprinting technique)
GSM reencoded
The following modifications failed to identify the correct song:
A one semitone pitch shift
A two semitone pitch shift
60 seconds removed from the needle
The original was also found. No failure analysis was done. The hay stack consists of about 100 hours of western pop, the needle is also a western pop song. If somebody wants to pick up this work or has an acoustic fingerprinting data set or drop me a line at .
The live demo we gave went well and we got a lot of positive, interesting feedback. The presentation about Tarsos is available here.
It was the first time in the history of ISMIR that there was a session with oral presentations about Non-Western Music. We were pleased to be part of this.
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.
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)
}
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 = {JorenSixandOlmoCornelis},
title = {Tarsos - a Platform to ExplorePitchScalesinNon-WesternandWesternMusic},
booktitle = {Proceedings of the 12th InternationalSocietyforMusicInformationRetrievalConference,
ISMIR2011},
year = {2011},
publisher = {InternationalSocietyforMusicInformationRetrieval}
}
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.
Naar jaarlijkse gewoonte wordt er in het Orpheus instituut de Dag van het Artistiek onderzoek georganiseerd. Hieronder volgt een tekstje over het onderzoeksproject rond Tarsos dat in het jaarboek komt. Het jaarboek is een boekje met daarin een overzicht van artistieke onderzoeksprojecten aan Vlaamse instituten. Het wordt gepubliceerd naar aanleiding van de eerder aangehaalde “Dag van het Artistiek Onderzoek”.
Het doel van dit onderzoeksproject is het ontwikkelen van een methode om een cultuuronafhankelijke kijk op muzikale parameters te verkrijgen. Meer concreet worden er technieken aangewend uit Music Information Retrieval om toonhoogte, tempo en timbre te bestuderen. Aanpassing van bestaande, meestal westers georiënteerde, MIR-methodes moet leiden tot een gestructureerde documentatie van verschillende klankkleuren, toonschalen, metrische verhoudingen en muzikale vormen. Die beschrijving kan dienen als inspiratie voor de ontwikkeling van een artistieke compsitionele taal of kan gebruikt worden als bronmateriaal voor wetenschappelijk onderzoek rond ethnische muziek. Bijvoorbeeld om (de eventuele
teloorgang van) de eigenheid van orale muziekculturen objectief aan te tonen.
In de eerste fase van het onderzoek ligt de focus van het onderzoek op één van de meer tastbare parameters: toonhoogte. In etnische muziek is het gebruik van toonhoogte vaak radicaal anders dan westerse muziek die meestal gebaseerd is op de onderverdeling van een octaaf in twaalf gelijke delen. Om toonladders uit
muziek te extraheren en weer te geven werd het software platform Tarsos ontwikkeld. Met Tarsos is het mogelijk om automatische toonladderanlyse uit te voeren op een grote dataset of om manueel een gedetailleerde analyse te verkrijgen van enkele muziekstukken. De cultuuronafhankelijke analysemethode waarvan Tarsos gebruik maakt kan even goed toegepast worden op Indonesische, Westerse of Afrikaanse muziek.
Onze bedoeling is om Tarsos te gebruiken om evoluties in toonladdergebruik te ontdekken in de enorme dataset van het Koninklijk Museum voor Midden-Afrika. Is toonladderdiversiteit in Afrika aan het wegkwijnen onder invloed van Westerse muziek? Zijn er specifieke kenmerken te vinden over eventueel ‘uitgestorven’ muziekculturen? Dit zijn vragen die kaderen in het overkoepelende onderzoeksproject van Olmo Cornelis en waar we met behulp van Tarsos een antwoord op proberen te vinden.
Later krijgen de twee overige muzikale parameters, tempo en timbre, een gelijkaardige behandeling. In de laatste fase van dit toch wel ambitieuze onderzoekproject wordt de relatie tussen de parameters onderzocht.
This post contains links to genuinely useful software to do signal based audio analysis.
Sonic Visualizer: As its name suggests Sonic Visualizer contains a lot different visualisations for audio. It can be used for analysis (pitch,beat,chroma,…) with VAMP-plugins. To quote “The aim of Sonic Visualiser is to be the first program you reach for when want to study a musical recording rather than simply listen to it”. It is the swiss army knife of audio analysis.
BeatRoot is designed specifically for one goal: beat tracking. It can be used for e.g. comparing tempi of different performances of the same piece or to track tempo deviation within one piece.
Tartini is capable to do real-time pitch analysis of sound. You can e.g. play into a microphone with a violin and see the harmonics you produce and adapt you playing style based on visual feedback. It also contains a pitch deviation measuring apparatus to analyse vibrato.
Tarsos is software for tone scale analysis. It is useful to extract tone scales from audio. Different tuning systems can be seen, extracted and compared. It also contains the ability to play along with the original song with a tuned midi keyboard .
Melodic Match is a different beast. It does not work on signal level but processes symbolic audio. More to the point it searches through MusicXML files – which can be created from MIDI-files. See its website for use cases. Melodic Match is only available for Windows.
Yesterday Tarsos was publicly presented at the symposium Perspectives for Computational Musicology in Amsterdam. The first public presentation of Tarsos, excluding this website. The symposium was organized by the Meertens Institute on the occasion of Peter van Kranenburg’s PhD defense.
The presentation included a live demo of a daily build of Tarsos (a Friday evening build) which worked, surprisingly, without hiccups. The presentation was done by Olmo Cornelis. This was the small introduction:
Tarsos – a Platform for Pitch Analysis of Ethnic Music
Ethnic music is a vulnerable cultural heritage that has received only recently more attention within the Music Information Retrieval community. However, access to ethnic music remains problematic, as this music does not always correspond to the Western concepts of music and metadata that underlie the currently available content-based methods. During this lecture, we like to present our current research on pitch analysis of African music. TARSOS, a platform for analysis, will be presented as a powerful tool that can describe and compare scales with great detail.
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, 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 Tarsos JAR Package.
Currently Tarsos has a Command Line Interface. An example with the files you can find attached:
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 method also has some limitations. Because audio is rendered there is no (background) noise, no fluctuations in pitch and timbre,… all of which are present in recorded audio. So testing testing tone scale extraction algorithms on recorded audio remains advised.
Tarsos can be used to search for music that uses a certain tone scale or tone interval(s). Tone scales can be defined by a Scala tone scale file or an exemplifying audio file. This text explains how you can use Tarsos for this task.
Search Using Scala Tone Scale Files
Scala files are text files with information about a tone scale. It is used to share and exchange tone scales. The file format originates from the Scala program :
Scala is a powerful software tool for experimentation with musical tunings, such as just intonation scales, equal and historical temperaments, microtonal and macrotonal scales, and non-Western scales. It supports scale creation, editing, comparison, analysis, …
Tarsos also understands Scala files. It is able to create a pitch class histogram using a gaussian mixture model. A technique described in A. C. Gedik, B.Bozkurt, 2010, "Pitch Frequency Histogram Based Music Information Retrieval for Turkish Music ", Signal Processing, vol.10, pp.1049-1063. (doi:10.106/j.sigpro.2009.06.017).
An example should make things clear. Lets search for an interval of 300 cents or exactly three semitones. A scala file with this interval is easy to define:
! example.scl
! An example of a tone interval of 300 cents
Tone interval of 300 cents
2
!
9001200.0
The next step is to create a histogram with an interval of 300 cents. In the block diagram this step is called “Peak histogram creation”. The Similarity calculation step expects a list of histograms to compare with the newly defined histogram. Feeding the similarity calculation with the western12ET tone scale and a pentatonic Indonesian Slendro tone scale shows that a 300 cents interval is used in the western tone scale but is not available in the Slendro tone scale.
This example only uses scala files, creating histograms is actually not needed: calculating intervals can be done using the scala file itself. This changes when audio files are compared with each other or with scala files.
Search Using Audio Files
When audio files are fed to the algorithm additional steps need to be taken.
First of all pitch detection is executed on the audio file. Currently two pitch extractors are implemented in pure Java, it is also possible to use an external pitch extractor such as aubio
Using pitch annotations a Pitch Histogram is created.
Peak detection on the Pitch Histogram results in a number of peaks, these should represent the distinct pitch classes used in the musical piece.
With the pitch classes a clean peak histogram is created during the Peak Histogram construction phase.
Finally the Peak histogram is matched with other histograms.
The last two steps are the same for audio files or scala files.
Using real audio files can cause dirty histograms. Determining how many distinct pitch classes are used is no trivial task, even for an expert (human) listener. Tarsos should provide a semi-automatic way of peak extraction: a best guess by an algorithm that can easily be corrected by a user. For the moment Tarsos does not allow manual intervention.
Tarsos
To use tarsos you need a recent java runtime (1.6) and the following command line arguments:
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:
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.
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.
Automatic comparison of human music, speech, and bird song suggests uniqueness of human scales by Jiei Kuroyanagi, Shoichiro Sato, Meng-Jou Ho, Gakuto Chiba, Joren Six, Peter Pfordresher, Adam Tierney, Shinya Fujii and Patrick Savage
Automatic comparison of global children’s and adult songs by Shoichiro Sato, Joren Six, Peter Pfordresher, Shinya Fujii and Patrick Savage
Presentation, Music Information Retrieval, Research papers, UGent, and Folk Music Analysis (FMA) conference
Thanks to the support of a travel grant by the faculty of Arts and Philosophy of Ghent University I was able to attend the 
musical_scales_extended_abstract__0826_revision_copy.pdf, animated_logo.gif, and ismir_2018_logo.jpg
As an extension of the 
I have contributed to the 4th International Digital Libraries for Musicology workshop (DLfM 2017) which was organized in Shanghai, China. It was a satellite event of the 
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 
What follows is about the 
Tarsos contains a couple of useful command line applications. They can be used to execute common tasks on lots of files. 
The extended abstract about PeachNote Piano has been accepted as a demonstration presentation to appear at the 
In de eerste fase van het onderzoek ligt de focus van het onderzoek op één van de meer tastbare parameters: toonhoogte. In etnische muziek is het gebruik van toonhoogte vaak radicaal anders dan westerse muziek die meestal gebaseerd is op de onderverdeling van een octaaf in twaalf gelijke delen. Om toonladders uit
Yesterday Tarsos was publicly presented at the symposium 
