Computational Corpus Analysis: A Case Study on Jazz Solos

For musicological studies on large corpora, the compilation of suitable data constitutes a time-consuming step. In particular, this is true for high-quality symbolic representations that are generated manually in a tedious process. A recent study on Western classical music has shown that musical phenomena such as the evolution of tonal complexity over history can also be analyzed on the basis of audio recordings. As our first contribution, we transfer this corpus analysis method to jazz music using the Weimar Jazz Database, which contains high-level symbolic transcriptions of jazz solos along with the audio recordings. Second, we investigate the influence of the input representation type on the corpus-level observations. In our experiments, all representation types led to qualitatively similar results. We conclude that audio recordings can build a reasonable basis for conducting such type of corpus analysis.