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higher delays by slowing down their tempo. This con-
clusion is also supported by the analysis of the QoME
questionnaires, reported in (Tsioutas et al., 2021).
Our work continues with a deeper analysis of
the audio data collected, focusing on issues such as
the dependence of tempo variations on other factors,
such as the style of music performed and the target
tempo of each piece. By grouping similar perfor-
mances together, we hope to be able to derive quan-
titative expressions of the relationship between delay
and tempo, depending on those factors.
Similarly, we are currently analyzing the video
data gathered during the sessions via machine learn-
ing techniques, and specifically facial emotion recog-
nition, in an attempt to quantify the emotional re-
sponse of the participants in an NMP session to delay.
ACKNOWLEDGEMENTS
We would like to thank all the participating musicians
for their patience during the experiments, as well as
the fellows who helped with setting up and carrying
out the experiments. We would also like to thank our
colleague Dr. Ioannis Doumanis (Lecturer at the Uni-
versity of Central Lancashire) for help in designing
the experiments.
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