Archiv der Kategorie: MIR Research

Online survey on music recommendation

Publiziert am 13. November 2016 von Igor Vatolkin

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 … Weiterlesen →

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Music data analysis book is published

Publiziert am 31. Oktober 2016 von Igor Vatolkin

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 http://sig-ma.de/music-data-analysis-book

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Bachelor thesis on the impact of cepstral features for emotion recognition

Publiziert am 13. Juli 2016 von Igor Vatolkin

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 … Weiterlesen →

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Diploma thesis on feature processing for classification of instruments and genres

Publiziert am 7. Juli 2016 von Igor Vatolkin

The task of the thesis „Ettiboa Adouakou: Zur Bedeutung verlaufspezifischer Merkmale bei Klassifikationsproblemen auf Musiksignaldaten“ (On the meaning of time-based feature aggregation for classification of audio signal data), PDF in German) was to compare several groups of time-based feature aggregation … Weiterlesen →

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Music data analysis book can be pre-ordered now!

Publiziert am 20. Mai 2016 von Igor Vatolkin

Weihs, C., Jannach, D., Vatolkin, I., Rudolph, G. (Eds.): Music data analysis: foundations and applications. https://www.crcpress.com/Music-Data-Analysis-Foundations-and-Applications/Weihs-Jannach-Vatolkin-Rudolph/p/book/9781498719568 This book edited and co-authored by SIGMA members and several partners provides a comprehensive overview of music data analysis, from introductory material to advanced … Weiterlesen →

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UMAP paper accepted

Publiziert am 16. April 2016 von Igor Vatolkin

Jannach, D., Kamehkhosch, I., Bonnin, G.: Biases in Automated Music Playlist Generation: A Comparison of Next-Track Recommending Techniques, User Modeling, Adaptation and Personalization (UMAP 2016), Halifax, CA, 2016 In this work, the results of a multi-metric comparison of different academic … Weiterlesen →

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Master thesis on lyrics finished

Publiziert am 10. April 2016 von Igor Vatolkin

Florian Treinat: Verwendung von Lyrics zur Generierung von Musik-Playlisten (Application of lyrics for generation of music playlists) (supervisors: Dietmar Jannach, Iman Kamehkhosh, e-Services Research Group, TU Dortmund) The goal of this work was to improve the quality of music recommendations … Weiterlesen →

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Master thesis on Twitter music data finished

Publiziert am 1. Februar 2016 von Igor Vatolkin

Mike Gösker: #nowplaying: Analyse musikbezogener Twitterdaten (Analysis of music-related Twitter data) (supervisors: Dietmar Jannach, Lukas Lerche, e-Services Research Group, TU Dortmund) In his master’s thesis Mike Gösker implemented and evaluated a set of techniques to generate music track recommendations based … Weiterlesen →

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Bachelor thesis on generation of drum accompaniment to improvised Jazz solos

Publiziert am 5. Dezember 2015 von Igor Vatolkin

The goal of the thesis (PDF in German) was to develop a system for the automatic generation of drum accompaniment to improvised Jazz solos. This is solved by means of an evolutionary algorithm. A list of rules is defined for … Weiterlesen →

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Master thesis on music restructuring finished

Publiziert am 10. September 2015 von Igor Vatolkin

This thesis addressed the problem of automatically rearranging any given music piece based on user-defined constraints. Rearranged music pieces are generated by playing back the original piece and jumping from one position in the original to another at specific times. … Weiterlesen →

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