Local Key Estimation In Classical Music Recordings: A Cross-Version Study on Schubert’s Winterreise - Schreiber, Hendrik; Weiß, Christof; Müller, Meinard (ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2020)
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.<br
Keywords: Training; Hidden Markov models; Estimation; Music; Training data; Harmonic analysis; Task analysis; music information retrieval; local key estimation; harmony analysis; evaluation; deep neural networks