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In this paper, we present 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, we 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. We introduce a hybrid annotation scheme that combines the use of title keys with expert validation and correction of only the challenging cases. The expert musicians also provide ratings of key recognition difficulty. Other meta-data include instrumentation. As demonstration of use of the dataset, and to provide initial benchmark comparisons for evaluating new algorithms, we conduct a series of experiments reporting key determination accuracy of four state-of-the-art algorithms. We further show the importance of considering factors such as estimated tuning frequency, key strength or confidence value, and key recognition difficulty in key finding. In the future, we plan to expand the dataset to include meta-data for other music information retrieval tasks.

The KUSC Classical Music Dataset for Audio Key Finding