## Extraction of Audio Descriptors and Their Evaluation in Music Classification Tasks

• Music Information Retrieval (MIR) is an interdisciplinary research area that has the goal to improve the way music is accessible through information systems. One important part of MIR is the research for algorithms to extract meaningful information (called feature data) from music audio signals. Feature data can for example be used for content based genre classification of music pieces. This masters thesis contributes in three ways to the current state of the art: • First, an overview of many of the features that are being used in MIR applications is given. These methods – called “descriptors” or “features” in this thesis – are discussed in depth, giving a literature review and for most of them illustrations. • Second, a large part of the described features are implemented in a uniform framework, called T-Toolbox which is programmed in the Matlab environment. It also allows to do classification experiments and descriptor visualisation. For classification, an interface to the machine-learning environment WEKA is provided. • Third, preliminary evaluations are done investigating how well these methods are suited for automatically classifying music according to categorizations such as genre, mood, and perceived complexity. This evaluation is done using the descriptors implemented in the T-Toolbox, and several state-of-the-art machine learning algorithms. It turns out that – in the experimental setup of this thesis – the treated descriptors are not capable to reliably discriminate between the classes of most examined categorizations; but there is an indication that these results could be improved by developing more elaborate techniques.

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