Here we use empirical methods to quantify and understand various aspects of music performance. This empirical approach has two important strengths. First, it provides a more robust alternative to subjective observation, forcing our observations to be based on concrete, measurable quantities, and allowing us to measure certain phenomena that are too subtle to perceive with our senses. Second, it helps us to scale up the study of musical performances to large datasets, helping us to compare many performances or many performers at once.
We pursue two main methodological approaches. The first involves analyzing commercial recordings, which offers access to abundant high-quality data and enables rich large-scale analyses. However, these recordings typically provide limited data sources—primarily audio and occasionally video—which may not suffice for certain research questions.
The second approach involves collecting data directly from live performances. This method allows us to gather comprehensive data, including MIDI output, physiological measurements, and performer questionnaire responses. With complete oversight of the data collection process, we can better control for potential confounding variables such as practice time, audience presence, and post-performance editing.
Huw Cheston's (PhD, 2021-) research exemplifies both approaches in studying jazz trio performances. His initial study focused on in-person data collection, examining jazz trios performing in a laboratory environment designed to simulate various teleconference conditions. His subsequent research has involved analyzing extensive datasets of publicly available performances by renowned jazz trios, employing advanced signal-processing and machine-learning techniques to extract performance features that would traditionally have required painstaking manual analysis.
More recently, Katelyn Emerson (PhD, 2023) is applying similar methodologies to classical organ performances. She is building a comprehensive dataset of organ performances by world-class musicians at two Cambridge College chapels. Her data collection encompasses a wide spectrum of measurements, including audio, video, MIDI data, questionnaires, and structured interviews. This rich dataset will inform various research questions about performance practice, particularly examining the complex interplay between performer, instrument, and venue.
Example publications
Cheston, H., Schlichting, J. L., Cross, I., & Harrison, P. M. C. (2024). Rhythmic qualities of jazz improvisation predict performer identity and style in source-separated audio recordings. Royal Society Open Science. https://doi.org/10.1098/rsos.240920
Cheston, Huw, Cross, I., & MC Harrison, P. (2024). Trade-offs in coordination strategies for duet jazz performances subject to network delay and jitter. Music Perception, 42(1), 48–72. https://doi.org/10.1525/mp.2024.42.1.48
Cheston, Huw, Schlichting, J. L., Cross, I., & Harrison, P. M. C. (2024). Jazz Trio Database: Automated annotation of jazz piano trio recordings processed using audio source separation. Transactions of the International Society for Music Information Retrieval, 7(1), 144–158. https://doi.org/10.5334/tismir.186
