Abstract
In this article, the authors have presented a benchmark dataset based on the KUSC classical music collection and provide baseline key-finding comparison results. Audio key finding is a basic music information retrieval task; it forms an essential component of systems for music segmentation, similarity assessment, and mood detection. Due to copyright restrictions and a labor-intensive annotation process, audio key finding algorithms have only been evaluated, using small proprietary datasets to date. To create a common base for systematic comparisons, they have constructed a dataset comprising of more than 3,000 excerpts of classical music. The excerpts are made publicly accessible via commonly used acoustic features such as pitch-based spectrograms and chromagrams. The authors have introduced a hybrid annotation scheme that combines the use of title keys with expert validation and correction of only the challenging cases. They further show the importance of considering factors, such as estimated tuning frequency, key strength or confidence value, and key recognition difficulty in key finding.