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Abstract:
The goal of music information retrieval (MIR) is to develop novel strategies
and techniques for organizing, exploring, accessing, and understanding music
data in an efficient manner.
The conversion of waveform-based audio data into semantically meaningful
feature representations by the use of digital signal processing techniques is
at the center of MIR and constitutes a difficult field of research because of
the complexity and diversity of music signals.
In this thesis, we introduce novel signal processing methods
that allow for extracting musically meaningful information from audio signals.
As main strategy, we exploit musical knowledge about the signals' properties to
derive feature representations that show a significant degree of robustness
against musical variations but still exhibit a high musical expressiveness. We
apply this general strategy to three different areas of MIR:
Firstly, we introduce novel techniques for extracting tempo and beat
information, where we particularly consider challenging music with changing
tempo and soft note onsets. Secondly, we present novel algorithms for the
automated segmentation and analysis of folk song field recordings, where one
has to cope with significant fluctuations in intonation and tempo as well as
recording artifacts. Thirdly, we explore a cross-version approach
to content-based music retrieval based on the query-by-example paradigm. In all
three areas, we focus on application scenarios where strong musical variations
make the extraction of musically meaningful information a challenging task.
