direkt zum Inhalt springen

direkt zum Hauptnavigationsmenü

Sie sind hier

TU Berlin

Inhalt des Dokuments

Martin Herzog






born in Berlin, Germany, August 20th 
Study of Computer Science at Humboldt University Berlin
(Diploma 2011)
Diploma thesis: „Harmony Analysis of MIDI Data as a Basis for the Extraction of Harmonic High-Level-Features“ 
since 2011
]init[ AG for Digital Communication,
Current position: Senior IT Consultant
since 2016
Research associate at TU Berlin
Project: ABC_DJ (Artist-to-Business-to-Business-to-Consumer Audio Branding System)
PhD project: Predicting Musical Meaning from High-Level Music Features

PhD Project

Predicting Musical Meaning from High-Level Music Features

What is the link between musical content on one hand and perceived musical meaning on the other?

In my PhD project I investigate the question which features – derived from music theory – play an important role in predicting perceived musical meaning. Using data from a large-scale lsitening experiment, I aim to employ a variety of Machine Learning techniques for the prediction of perceived semantics in popular music.

Supervisors: Prof. Dr. Stefan Weinzierl (TU Berlin), Prof. Dr. Hauke Egermann (TU Dortmund)

Research Interests

  • Music perception and processing
  • Musical meaning
  • Music and emotion
  • Music information retrieval
  • Audio branding


Herzog, M., Lepa, S., Egermann, H., Schoenrock, A., & Steffens, J. (2020). Towards a common terminology for music branding campaigns. Journal of Marketing Management.

Lepa, S., Herzog, M., Steffens, J., Schoenrock, A., & Egermann, H. (2020). A computational model for predicting perceived musical expression in branding scenarios. Journal of New Music Research.

Lepa, S., Steffens, J., Herzog, M., & Egermann, H. (2020). Popular Music as Entertainment Communication: How Perceived Semantic Expression Explains Liking of Previously Unknown Music. Media and Communication.

Herzog, M., Lepa, S., Steffens, J., Schönrock, A. & Egermann, H. (2017). Predicting musical meaning in audio branding scenarios. Proceedings of the 25th Anniversary Conference of the European Society for Cognitive Science of Music (ESCOM 2017), Ghent, Belgium, 2017.

Steffens, J., Lepa, S., Herzog, M., Schönrock, A., Peeters, G., & Egermann, H. (2017). High-level chord features extracted from audio can predict perceived musical expression. Proceedings of the 18th International Society for Music Information Retrieval Conference (ISMIR 2017), Suzhou, China, 2017.

Steffens, J., Lepa, S., Egermann, H., Schönrock, A. & Herzog, M. (2017). Entwicklung eines Systems zur automatischen Musikempfehlung im Kontext des Audio Brandings. In: Fortschritte der Akustik: Tagungsband d. 43. DAGA. Deutsche Gesellschaft für Akustik, 2017.

Herzog, M., Lepa, S. & Egermann, H. (2016). Towards automatic music recommendation for audio branding scenarios. Proceedings of the ISMIR 2016 conference, New York, USA, 2016.

Zusatzinformationen / Extras


Schnellnavigation zur Seite über Nummerneingabe

Diese Seite verwendet Matomo für anonymisierte Webanalysen. Mehr Informationen und Opt-Out-Möglichkeiten unter Datenschutz.