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.

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. Partly, this may be due to its inherent ambiguity and subjectivity, which makes annotating local keys a challenging task. In this article, we approach local key estimation with a cross-version dataset comprising nine performances (versions) of Schubert's song cycle Winterreise annotated by three different music theory experts. We consider two baseline methods that are representative for common types of signal processing algorithms: an HMM-based system and 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 a central 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, we obtain substantially higher accuracies, especially for the CNN, which seems to effectively learn the harmonic progressions of the songs (“cover song effect”) and successfully generalizes to unseen versions. We further discuss the results for several songs in detail and assess our results from the perspective of multiple annotators. This cross-annotator study reveals that a substantial part of the systems' errors coincides with annotator disagreement and an even larger part can be traced back to musically explainable relationships among different keys.

Tempo estimation is a fundamental problem in music information retrieval. Most approaches attempt to solve two problems: first finding a dominant pulse and second correcting the metrical level of this pulse. The latter has also been dubbed fixing the octave error. We propose an algorithm for tempo estimation that addresses both problems mostly independently. While using a standard pulse detection technique, for octave error correction, we exploit a simple relationship between a single global feature, average spectral novelty, and listener perception of musical tempo. The proposed method is extremely simple. Nevertheless, it outperforms most existing tempo estimation methods and is on par with the best-performing ones. It thus exemplifies that a global feature-based approach can significantly improve tempo estimation.

In view of rapidly growing digital music collections and ubiquitous music consumption, the development of technologies for identifying, browsing, and managing audio content has become a major strand of research. In this context, audio identification (ID) systems for identifying audio recordings by means of short query audio clips have become of commercial relevance. In this paper, we take a closer look at a widely used audio ID system originally developed by Haitsma and Kalker and propose several modifications that yield significant improvements with regard to retrieval speed and storage requirements. As the main contribution, we introduce a measure that establishes a connection between the temporal correlation of hash values (used for indexing) and their ability to survive in the presence of noise and signal distortions. Based on this measure, we improve the overall performance of the audio ID system by means of four strategies. First, we change the way fingerprints (audio features) are generated to increase their reliability. Second, by prioritizing more reliable hash values when searching for reference entries, we achieve substantial gains in retrieval speed by a factor of almost seven. Third, by enlarging the query fingerprint, we increase our chances of identifying reliable hash values. Fourth, by indexing only the most reliable hashes, thus applying a sub-sampling strategy, we significantly lower the server side storage requirements by a factor of ten.