The complexity of music is one of the less intensively researched areas in music information retrieval so far. Although very interesting findings have been reported over the years, there is a lack of a unified approach to the matter. Relevant publications mostly concentrate on single aspects only and are scattered across different disciplines. Especially an automated estimation based on the audio material itself has hardly been addressed in the past. However, it is not only an interesting and challenging topic, it also allows for very practical applications.|
The motivation for the presented research lies in the enhancement of human interaction with digital music collections. As we will discuss, there is a variety of tasks to be considered, such as collection visualization, play-list generation, or the automatic recommendation of music. While this thesis doesnít deal with any of these problems in deep detail it aims to provide a useful contribution to their solution in form of a set of music complexity descriptors. The relevance of music complexity in this context will be emphasized by an extensive review of studies and scientific publications from related disciplines, like music psychology, musicology, information theory, or music information retrieval.
This thesis proposes a set of algorithms that can be used to compute estimates of music complexity facets from musical audio signals. They focus on aspects of acoustics, rhythm, timbre, and tonality. Music complexity is thereby considered on the coarse level of common agreement among human listeners. The target is to obtain complexity judgements through automatic computation that resemble a naive listenerís point of view. Expert knowledge of specialists in particular musical domains is therefore out of the scope of the proposed algorithms. While it is not claimed that this set of algorithms is complete or final, we will see a selection of evaluations that gives evidence to the usefulness and relevance of the proposed methods of computation. We will finally also take a look at possible future extensions and continuations for further improvement, like the consideration of complexity on the level of musical structure.