Start of the DFG-funded project on interpretable music segmentation

Today, the DFG-funded project “Evolutionary optimisation for interpretable music segmentation and music categorisation based on discretised semantic metafeatures” is started. The main goal of the project is to develop new methods for the extraction of interpretable structural information of audio recordings and to improve the classification of music into genres, styles, and emotions, see also the project description in the GEPRIS database.

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Interdisciplinary course on music data analysis in summer term 2018

The course on music data analysis (in German) based on the textbook Music Data Analysis: Foundations and Applications will again take place at TU Dortmund, this time at the Department of Computer Science and during the summer term (see also the course web page from the last year).

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Test persons are searched

We search for test persons with basic skills in jazz and improvisation, either guitar or piano players (1.5 hour experiment, 20€). Details in German are here.

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Job offer for a student assistant

For the project funded by the German Research Foundation, Evolutionary Optimisation of Music Categorisation based on Complex Structure Analysis of Audio Signals, a position as a student assistant (8 hours per week) is offered at the Chair of Algorithm Engineering, Department of Computer Science, TU Dortmund. Please see the full description in German / in English.

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SIGMA #41

The program of the following 41st SIGMA meeting on 20.12.2017, 14:00-16:00, which takes place at the Department of Computer Science, TU Dortmund, Otto-Hahn-Str. 14, room 202:

14:00-14:05 Welcome greetings

14:05-14:45 Master thesis
Kerstin Wintersohl: Simultaneous Onset and Pitch Detection

14:45-15:15 Conference paper
Daniel Stoller, Queen Mary University of London: Adversarial Semi-Supervised Audio Source Separation applied to Singing Voice Extraction

15:15-15:40 Software demo
Frederik Heerde: Annotation Editor for AMUSE

15:40-15:50 Planning of the lecture on music data analysis in summer term 2018

15:50-16:00 Miscellaneous, next meeting

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SIGMA #40

The program of the following 40th SIGMA meeting on 16.10.2017, 14:00-16:00, which takes place at Department of Statistics, TU Dortmund, Vogelpothsweg 78, room CDI 120:

14:00-14:05 Welcome greetings

14:05-14:45 Journal paper
René Grzeszick: Bag-of-Features Methods for Acoustic Event Detection and Classification

14:45-15:25 PhD thesis
Anil Nagathil: Music Signal Processing for the Reduction of Auditory distortions in Hearing-impaired Listeners

15:25-15:40 Planning of lecture on music data analysis in summer term 2018

15:40-15:50 Miscellaneous, next meeting

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AMUSE support of Java 1.8 and RapidMiner 5

Two recent updates (thanks to Frederik Heerde!):
- AMUSE can be started now with Java 1.8 (in the older version, using of Java 1.8 led to problems for classification with trained models).
- Integrated RapidMiner library was updated to version 5.

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Interdisciplinary course on music data analysis in summer term 2017

The course on music data analysis (in German) based on the textbook Music Data Analysis: Foundations and Applications will again take place at TU Dortmund, Department of Statistics in September 2017 (see also the course web page from the last year)

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SIGMA #39

The program of the following 39th SIGMA meeting on 25.07.2017, 14:00-16:00, which takes place at Faculty of Computer Science, TU Dortmund, Otto-Hahn-Str. 14, room E04
(talks in German):

14:00-14:05 Welcome greetings

14:05-14:35 Bachelor thesis
Andreas Jöbges: Optical recognition of bagpipe notation

14:35-15:05 Bachelor thesis
Oliver Magiera: Recognition of melodic patterns in symbolic music data

15:05-15:35 Project group
PG 607: Reactive Jukebox – state after the 1st term

15:35-15:55 Planning of lectures on music data analysis in summer terms 2017 and 2018

15:55-16:00 Miscellaneous, next meeting

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Bachelor thesis on optical recognition of bagpipe notation

The thesis “Andreas Jöbges: Optische Erkennung von Dudelsacknotation” (Optical recognition of bagpipe notation, PDF in German) presents a system which produces digital score in “BWW” format from scanned music pieces with the help of neural networks trained for the recognition of notes and specific symbols in bagpipe notation.

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