An optimization-based approach to key segmentation

Keys provide musical context and key modulation (changes) forms a crucial feature of music. In the age of big music data collections, automatic key segmentation is an important step towards music indexing and structure analysis. When using template-based key-finding methods, the best segmentation must minimize intra-segment distance to keys while maximizing inter-segment distance for neighboring keys. We present a general dynamic programming (DP) solution to this segmentation problem that is applicable to all distance-based key-finding methods and that does not require the number of segments to be pre-defined. This metaalgorithm is applied to the Kostka-Payne and Beatles datasets with three widely used distance-based key-finding methods. The key-finding results are evaluated using a compound score, and precision and recall. Statistical analysis of the results show that a precision value of 0.9 can be achieved with both datasets; for excerpts in one key, an average compound score above 0.8 is reported.