The Recorded Brahms Corpus (RBC): A Dataset of Performative Parameters in Recordings of Brahms's Cello Sonatas

This report describes the open-source Recorded Brahms Corpus (RBC) dataset, as well as the methods employed to extract and process the data. The dataset contains (micro)timing and dynamic data from 21 recordings of Brahms's Cello Sonatas, Opp. 38 and 99, focusing on note and beat onsets and duration, tempo fluctuations, and dynamic variations. Consistent manual annotation of the corpus in Sonic Visualiser was necessary prior to automatic extraction. Data for each recording and measurement unit are given as TXT files. Scores in various digital formats, the original SV files and diamond-shaped scape plots visualizations of the data are offered too. Expansion of the corpus with further movements of the sonatas, further recordings thereof and other compositions by Brahms is planned. The study of the data may contribute to performance studies and music theory alike.

Fractal Tempo Fluctuation and Pulse Prediction

WE INVESTIGATED PEOPLES' ABILITY TO ADAPT TO THE fluctuating tempi of music performance. In Experiment 1, four pieces from different musical styles were chosen, and performances were recorded from a skilled pianist who was instructed to play with natural expression. Spectral and rescaled range analyses on interbeat interval time-series revealed long-range (1/f type) serial correlations and fractal scaling in each piece. Stimuli for Experiment 2 included two of the performances from Experiment 1, with mechanical versions serving as controls. Participants tapped the beat at ¼¼- and ⅛⅛-note metrical levels, successfully adapting to large tempo fluctuations in both performances. Participants predicted the structured tempo fluctuations, with superior performance at the ¼¼-note level. Thus, listeners may exploit long-range correlations and fractal scaling to predict tempo changes in music.