Prof. Dr. Gerald Langner from TU Darmstadt will give a talk “Der neuronale Code von Tonhöhe, Klang und Harmonie” (The neural code of pitch, sound, and harmony) at the Institute of Music and Music Science, TU Dortmund on 6.7.2016, 14-16 PM, Emil-Figge-Str. 50, R 4.313.
Weihs, C., Jannach, D., Vatolkin, I., Rudolph, G. (Eds.): Music data analysis: foundations and applications.
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 concepts. It covers various applications including transcription and segmentation as well as chord and harmony, instrument and tempo recognition. It also discusses the implementation aspects of music data analysis such as architecture, user interface and hardware. It is ideal for use in university classes with an interest in music data analysis. It also could be used in computer science and statistics as well as musicology.
In winter term 2016/2017, two proseminars will take place at Chair of Algorithm Engineering, TU Dortmund:
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 approaches and a commercial playlisting service (of The Echo Nest) are reported. The results show that all tested techniques generate playlists with certain biases, e.g., towards very popular tracks, and often create playlists continuations that are quite different from those that are created by real users.
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 with the help of lyrics. The proposed approaches are based on (a) the textual similarity and (b) the conveyed sentiment of lyrics. The results show that lyrics-based techniques are more efficient when the seed tracks (e.g., the tracks from the recent listening history of the user) are thematically related or sentimentally homogeneous.
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 on user posts and profiles from the social networking service Twitter. The recommendation strategies exploit temporal characteristics of the social media posts and are compared with baseline techniques that use popularity and neighborhood information.
In winter term 2015/2016, a proseminar “Actual challenges in music data analysis” (website in German) will take place at Chair of Algorithm Engineering, TU Dortmund. The topics for student talks and works should represent various MIR research areas and are selected from the proceedings of ISMIR 2014.
Two papers were accepted for upcoming conferences:
D. Jannach, L. Lerche, I. Kamehkhosh: Beyond “Hitting the Hits” – Generating Coherent Music Playlist Continuations with the Right Tracks. Proceedings of the 9th ACM Conference on Recommender Systems (RecSys 2015), Vienna, 2015
I. Vatolkin, G. Rudolph, C. Weihs: Evaluation of Album Effect for Feature Selection in Music Genre Recognition. Proceedings of the 16th International Society for Music Information Retrieval Conference (ISMIR 2015), Malaga, 2015
A PhD position is available at the Institute of Communication Acoustics, Ruhr-Universität Bochum, Germany.
The PhD project aims at the development and the experimental evaluation of algorithms for music signal processing in hearing devices. The applicant must have completed a Master’s degree in electrical engineering (or must hold an equivalent degree) and should be fluent in German. More information about the position and the application procedure can be found here:
The call is open until February 27th.
This month two papers were accepted.
The first one will appear in post-proceedings of ECDA 2014 and compares audio features and playlist statistics for music classification:
I. Vatolkin, G. Bonnin, D. Jannach – Comparing Audio Features and Playlist Statistics for Music Classification
The second one is accepted for EvoMUSART 2015. The conference will take place on 8.4-10.4.2015 in Copenhagen, Denmark. The paper describes a method for the maximization of interpretability in music classification without a limitation to use high-level (semantic) features only:
I. Vatolkin, G. Rudolph, C. Weihs – Interpretability of Music Classification as a Criterion for Evolutionary Multi-Objective Feature Selection