Project group “The Reactive Jukebox” starts in summer term

The student project group “The Reactive Jukebox” (supervised by D. Jannach and I. Vatolkin) will start in summer term 2017.

The goal of the project is to develop an application for music recommendation with an interactive interface to listener requests. Such interaction should be done in an intuitive and user-friendly way, for instance providing feeback in a spoken language or describing music with the help of interpretable semantic features (“please recommend me faster songs with more distorted guitars and more vocal segments”). For the indexing of music, it is planned to integrate features of different sources, like community tags, meta data, audio descriptors, or symbolic characteristics.

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Two papers accepted for EvoMUSART

F. Ostermann, I. Vatolkin, G. Rudolph: Evaluation Rules for Evolutionary Generation of Drum Patterns in Jazz Solos. Proceedings of the 6th International Conference on Computational Intelligence in Music, Sound, Art and Design (EvoMUSART 2017), Amsterdam, 2017

I. Vatolkin: Generalisation Performance of Western Instrument Recognition Models in Polyphonic Mixtures with Ethnic Samples. Proceedings of the 6th International Conference on Computational Intelligence in Music, Sound, Art and Design (EvoMUSART 2017), Amsterdam, 2017

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Bachelor thesis on vocal recognition

The thesis “Florian Scholz: Gesangserkennung in polyphonen Musikaufnahmen” (Vocal recognition in polyphonic music recordings, PDF in German) compares several methods for supervised recognition of vocals in music tracks. In particular, experiments with training and test sets of different genres are investigated.

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Update: exercises for music data analysis textbook

On the music data analysis textbook web page, the exercises manual is now available for download.

Update 23.12.2016: The newer version of exercises and the list of errata were uploaded to the book web page.

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The program of the following 37th SIGMA meeting on 19.12.2016, 14:00-16:20, which takes place at Faculty of Statistics, TU Dortmund, Mathematics Building, room M/E27
(talks on bachelor theses in German):

14:00-14:05 Welcome greetings

14:05-14:30 Bachelor thesis
Alex Runge: Analysis of energy features for prediction of emotions in music

14:30-14:55 Bachelor thesis
Philipp Hallmeier: Modelling of frequency and amplitude modulation

14:55-15:20 Bachelor thesis + Demo
Fabian Ostermann: Generation of drum accompaniment to improvised Jazz solos

15:20-15:30 Short break

15:30-15:45 Bachelor thesis
Florian Scholz: Recognition of vocals in polyphonic music recordings

15:45-16:00 ISMIR conference paper
Daniel Stoller: Analysis and classification of phonation modes in singing

16:00-16:20 Coursebook on music data analysis, teaching exercises and slides, miscellaneous, next meeting

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Online survey on music recommendation

The e-Services research group of the computer science department, TU Dortmund is conducting a user study about automated music recommendation and would like to invite you to participate in an online experiment. The experiment takes about 10 minutes. You will be asked to listen to a few song excerpts and to give feedback on them. Start the experiment here: Thank you!

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Music data analysis book is published

The textbook on music data analysis is now published.

Weihs, C., Jannach, D., Vatolkin, I., Rudolph, G. (Eds.):
Music data analysis: foundations and applications

For the list of chapters, see

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Call for papers: GECCO 2017 in Berlin, Germany (15-19.07.2017)

The GECCO 2017 Program Committee invites the submission of technical papers describing your best work in genetic and evolutionary computation. As in previous GECCO conferences, the digital entertainment technologies and arts (DETA) track invites submissions describing original work involving the use of computation in the creative arts, including design, games, and music.

Abstracts need to be submitted by January 30, 2017. Full papers are due by the non-extensible deadline of February 6, 2017. Note that only full papers can be reviewed and accepted. The abstracts allow us to prepare better for the reviewing process.

Each paper submitted to GECCO will be rigorously evaluated in a double-blind review process. The evaluation is on a per-track basis, ensuring high interest and expertise of the reviewers. Review criteria include significance of the work, technical soundness, novelty, clarity, writing quality, and sufficiency of information to permit replication, if applicable. All accepted papers will be published in the ACM Digital Library.

By submitting a paper, the author(s) agree that, if their paper is accepted, they will:

  • submit a final, revised, camera-ready version to the publisher on or before the camera ready deadline,
  • register at least one author to attend the conference on or before the advance registration deadline,
  • attend the conference (at least one author), and
  • present the accepted paper at the conference.
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Course on music data analysis at TU Dortmund

In September 2016, the interdisciplinary course on music data analysis based on the textbook “Music Data Analysis: Foundations and Applications” took place at the Faculty of Statistics, TU Dortmund. The course contained lectures, exercises, and an a written examination. The participated students came from several faculties including statistics, computer science, data science, signal processing, electrical engineering, and music science. The list of lecture units (in German) is available on the course web site. In future, we plan to release the slides of the course to the SIGMA website of the book.

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Bachelor thesis on the impact of cepstral features for emotion recognition

In the thesis “Philipp Kramer: Relevanz cepstraler Merkmale für Vorhersagen im Arousal-Valence Modell auf Musiksignaldaten” (Relevance of cepstral features for predictions in arousal-valence model for music signals, PDF in German), several groups of features were analysed for the regression-based prediction of arousal and valence: cepstral, energy, timbral, harmonic/melodic, and temporal/rhythmic. Some parameters for the feature extraction were optimised, and it was shown that features from the cepstral domain belonged to the best models.

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