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Pages
Posts
portfolio
Jazz Performer Identification Model
An automated pipeline that uses deep-learning assisted audio signal processing to identify the performer playing on a given jazz recording.
Cambridge Jazz Trio Database
A dataset of 16 hours of commercial jazz recordings (piano, bass, and drums) annotated using an automated signal processing pipeline
Music Performance Manipulation Software
A new software platform for introducing controlled manipulations into multiple synchronised audio-video feeds during an experiment 
Teaching: Analysing Musical Performances
Teaching materials for undergraduate-level music performance analysis supervisions, introducing a variety of basic audio processing and statistical concepts
Teaching: Introduction to Music & Science workshop
Teaching materials for pre-university level workshop, introducing a variety of music information retrieval concepts in no-code interactive environments
publications
Rhythmic Qualities of Jazz Improvisation Predict Performer Identity and Style in Source-Separated Audio Recordings.
Published in Royal Society Open Science, 2024
We demonstrate that a supervised learning model trained solely on rhythmic features extracted from 300 source-separated audio recordings of jazz pianists was capable of identifying the performer in 52% of cases, over five times better than chance.
Recommended citation: Cheston, H., Schlichting, J. S., Cross, I., & Harrison, P. M. C. Rhythmic Qualities of Jazz Improvisation Predict Performer Identity and Style in Source-Separated Audio Recordings. Royal Society Open Science. 2024 (accepted, subject to minor revisions) https://doi.org/10.31234/osf.io/txy2f
Jazz Trio Database: Automated Timing Annotation of Jazz Piano Trio Recordings Processed Using Audio Source Separation
Published in Transactions of the International Society for Music Information Retrieval, 2024
We introduce the Jazz Trio Database, a dataset of 45 hours of jazz piano trio recordings with automatically generated annotations for every performer (piano soloist, bass and drums accompaniment) in the ensemble.
Recommended citation: Cheston H, Schlichting JL, Cross I, & Harrison PMC. Jazz Trio Database: Automated Annotation of Jazz Piano Trio Recordings Processed Using Audio Source Separation. Transactions of the International Society for Music Information Retrieval. 2024; 7(1), 144–158. https://doi.org/10.5334/tismir.186
Trade-offs in Coordination Strategies for Duet Jazz Performances Subject to Network Delay and Jitter
Published in Music Perception, 2024
We show that five duos of professional jazz musicians adopt diverse strategies when confronted by the difficulties of coordinating performances over a network — difficulties that are not exclusive to networked performance, but are also present in other situations (such as when coordinating performances over large physical spaces).
Recommended citation: Cheston H, Cross I, & Harrison PMC. Trade-offs in Coordination Strategies for Duet Jazz Performances Subject to Network Delay and Jitter. Music Perception. 2024; 42(1), 48–72. https://doi.org/10.1525/mp.2024.42.1.48
talks
“Turning the beat around”: Time, temporality, and participation in the jazz solo break
Published:
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Measuring the effects of variable latency timings and jitter on networked music performances
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The effects of variable latency timings and jitter on networked musical performances.
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Coordination strategies in networked jazz performances.
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Modelling coordination strategies in improvised musical performances by skilled jazz groups
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An automated pipeline for characterizing timing in jazz trios.
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teaching
Supervisor and Guest Lecturer, Topics in Music & Science
Undergraduate course, University of Cambridge, Faculty of Music, 2021
- Designed & delivered small-group supervisions on analysis of audio recordings
- Designed & delivered lecture on analysing audio recordings, involving introduction and demonstration of basic Music Information Retrieval concepts.
Supervisor and Guest Lecturer, Topics in Music & Science
Undergraduate course, University of Cambridge, Faculty of Music, 2022
- Designed & delivered small-group supervisions for Undergraduate students on: (i) analysing audio recordings, (ii) introduction to programming in Python and R, (iii) visualising and simulating data, (iv) overall course revision
- Designed & delivered lecture on analysing audio recordings, involving introduction and demonstration of basic Music Information Retrieval concepts.
Workshop Leader, Music & Science
Pre-University Summer School, Sutton Trust, 2023
- Delivered workshop on audio signal analysis for summer school participants (A-Level equivalent)
Supervisor and Guest Lecturer, Topics in Music & Science
Undergraduate course, University of Cambridge, Faculty of Music, 2023
- Designed & delivered small-group supervisions for Undergraduate students on: (i) analysing audio recordings, (ii) designing a research proposal, (iii) overall course revision
- Designed & delivered lecture on analysing audio recordings, involving introduction and demonstration of basic Music Information Retrieval concepts.
Dissertation co-superviser
Undergraduate course, University of Cambridge, Faculty of Music, 2023
- Co-supervised final year undergraduate dissertation project on the automated harmonic analysis of jazz piano recordings in Cambridge Jazz Trio Database (Cheston et al., 2024).