Verena Konz

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A performance of a piece of music heavily depends on the musician's or conductor's individual vision and personal interpretation of the given musical score. As basis for the analysis of artistic idiosyncrasies, one requires accurate annotations that reveal the exact timing and intensity of the various note events occurring in the performances. In the case(More)
The automated extraction of chord labels from audio recordings constitutes a major task in music information retrieval. To evaluate computer-based chord labeling procedures , one requires ground truth annotations for the underlying audio material. However, the manual generation of such annotations on the basis of audio recordings is tedious and(More)
—For a given piece of music, there often exist multiple versions belonging to the symbolic (e.g., MIDI representations), acoustic (audio recordings), or visual (sheet music) domain. Each type of information allows for applying specialized, domain-specific approaches to music analysis tasks. In this paper, we formulate the idea of a cross-version analysis(More)
In the field of music information retrieval (MIR), great efforts have been directed towards the development of technologies and interfaces that allow users to access and explore music on an unprecedented scale. On the other hand, musicians and music teachers are often still skeptical about the benefits of computer-based methods in music education. In this(More)
The automated extraction of chord labels from audio recordings is a central task in music information retrieval. Here, the chord labeling is typically performed on a specific audio version of a piece of music, produced under certain recording conditions, played on specific instruments and characterized by individual styles of the musicians. As a(More)
In view of the exploding distribution of digitized audio material, computer-based methods have become indispensable for processing and analyzing the content of music signals. To evaluate analysis results obtained by automated methods, one requires manually generated high-quality labeled data and the feedback by music experts. In this paper, we introduce(More)
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