Fuzzy Frequencies: Finding Tonal Structures in Audio Recordings of Renaissance Polyphony

Understanding tonal structures in Renaissance music has been a long-standing musicological problem. Computational analysis on a large scale could shed new light on this. Encoded scores provide easy access to pitch content, but the availability of such data is low. This paper addresses this shortage of data by exploring the potential of audio recordings. Analysing audio, however, is challenging due to the presence of harmonics, reverb and noise, which may obscure the pitch content. We test several multiple pitch estimation models on audio recordings, using encoded scores from the Josquin Research Project (JRP) as a benchmark for evaluation. We present a dataset of multiple pitch estimations from 611 compositions in the JRP. We use the pitch estimations to create pitch profiles and pitch class profiles, and to estimate the lowest final pitch of each recording. Our findings indicate that the Multif0 model yields pitch profiles, pitch class profiles and finals most closely aligned with symbolic encodings. Furthermore, we found no effect of year of recording, number of voices and ensemble composition on the accuracy of pitch estimations. Finally, we demonstrate how these models can be applied to gain insight into tonal structures in early polyphony.