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.
Local Key Estimation In Classical Music Recordings: A Cross-Version Study on Schubert’s Winterreise
Hendrik Schreiber,Christof Weiss,Meinard Müller
Published 2020 in IEEE International Conference on Acoustics, Speech, and Signal Processing
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- Publication year
2020
- Venue
IEEE International Conference on Acoustics, Speech, and Signal Processing
- Publication date
2020-05-01
- Fields of study
Art, Computer Science
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