Evolutionary optimisation for interpretable music segmentation and music categorisation based on discretised semantic metafeatures

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Similarity and repetition belong to the basic principles of music composition, but also to formative influence factors on the perception and the categorisation of music. The task of this project is to develop new methods for the extraction of structural information of audio recordings and to improve the classification of music into genres, styles, and emotions.In contrast to established signal analysis approaches, the main focus is the extraction of interpretable features which are related to music theory. This holds for the segmentation based on several criteria (instrumentation, harmony, tempo, rhythm, and dynamics) as well as for the estimation of semantic characteristics of the segments for music categorisation.The complement goals are to increase the classification quality and to reduce the demands on required training data. Besides, the classification models will be optimised with respect to the robustness (application on signals of reduced quality) and the generalisation performance (measured as the deviation of classification quality for music pieces from different categories).Evolutionary algorithms and their adaptation with novel, problem-related extensions are chosen as the core methods for the optimisation of feature extraction, feature selection, and multi-objective optimisation of segmentation and music categorisation.