Local Key Estimation In Classical Music Recordings: A Cross-Version Study on Schubert’s Winterreise

Schreiber H, Weiß C, Müller M (2020)


Publication Type: Conference contribution

Publication year: 2020

Publisher: IEEE

Conference Proceedings Title: ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Event location: Barcelona ES

DOI: 10.1109/ICASSP40776.2020.9054642

Abstract

While global key and chord estimation for both popular and classical music recordings have received a lot of attention, little research has been devoted to estimating the local key for classical music. In this work, we approach local key estimation on a unique cross-version dataset comprising nine performances (versions) of Schubert's song cycle Winterreise-a challenging scenario of high musical ambiguity and subjectivity. We compare an HMM-based system with a CNN-based approach. For both models, we employ a similar training procedure including the optimization of hyperparameters on a validation split. We systematically evaluate the model predictions and provide musical explanations for key confusions. As our main contribution, we explore how different training-test splits affect the models' efficacy. Splitting along the song axis, we find that both methods perform similarly well. Splitting along the version axis leads to clearly higher results, especially for the CNN, which seems to effectively learn the harmonic progressions of the songs ("cover song effect") and successfully generalizes to unseen versions.

Authors with CRIS profile

How to cite

APA:

Schreiber, H., Weiß, C., & Müller, M. (2020). Local Key Estimation In Classical Music Recordings: A Cross-Version Study on Schubert’s Winterreise. In ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Barcelona, ES: IEEE.

MLA:

Schreiber, Hendrik, Christof Weiß, and Meinard Müller. "Local Key Estimation In Classical Music Recordings: A Cross-Version Study on Schubert’s Winterreise." Proceedings of the 2020 IEEE International Conference on Acoustics, Speech and Signal Processing, Barcelona IEEE, 2020.

BibTeX: Download