Automated methods for audio-based music analysis with applications to musicology

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This thesis contributes to bridging the gap between music information retrieval (MIR) and musicology. We present several automated methods for music analysis, which are motivated by concrete application scenarios being of central importance in musicology. In this context, the automated music analysis is performed on the basis of audio material. Here, one reason is that for a given piece of music usually many different recorded performances exist. The availability of multiple versions of a piece of music is exploited in this thesis to stabilize analysis results. We show how the presented automated methods open up new possibilities for supporting musicologists in their work. Furthermore, we introduce novel interdisciplinary concepts which facilitate the collaboration between computer scientists and musicologists. Based on these concepts, we demonstrate how MIR researchers and musicologists may greatly benefit from each other in an interdisciplinary collaboration. Firstly, we present a fully automatic approach for the extraction of tempo parameters from audio recordings and show to which extent this approach may support musicologists in analyzing recorded performances. Secondly, we introduce novel user interfaces which are aimed at encouraging the exchange between computer science and musicology. In this context, we indicate the potential of computer-based methods in music education by testing and evaluating a novel MIR user interface at the University of Music Saarbr ücken. Furthermore, we show how a novel multi-perspective user interface allows for interactively viewing and evaluating version-dependent analysis results and opens up new possibilities for interdisciplinary collaborations. Thirdly, we present a cross-version approach for harmonic analysis of audio recordings and demonstrate how this approach enables musicologists to explore harmonic structures even across large music corpora. Here, one simple yet important conceptual contribution is to convert the physical time axis of an audio recording into a performance-independent musical time axis given in bars.