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AD. J.. Hill, J. Mulder, J. Burton, M. Kok, and M. Lawrence, "Sound Level Monitoring at Live Events, Part 1--Live Dynamic Range," J. Audio Eng. Soc., vol. 69, no. 11, pp. 782-792, (2021 November.). doi: https://doi.org/10.17743/jaes.2021.0050 AD. J.. Hill, J. Mulder, J. Burton, M. Kok, and M. Lawrence, "Sound Level Monitoring at Live Events, Part 1--Live Dynamic Range," J. Audio Eng. Soc., vol. 69 Issue 11 pp. 782-792, (2021 November.). doi: https://doi.org/10.17743/jaes.2021.0050
Abstract: Musical dynamics are often central within pieces of music and are therefore likely to be fundamental to the live event listening experience. While metrics exist in broadcasting and recording to quantify dynamics, such measures work on high-resolution data. Live event sound level monitoring data is typically low-resolution (logged at one second intervals or less), which necessitates bespoke musical dynamics quantification. Live dynamic range (LDR) is presented and validated here to serve this purpose, wheremeasurement data is conditioned to remove song breaks and sound level regulation-imposed adjustments to extract the true musical dynamics from a live performance. Results show consistent objective performance of the algorithm, as tested on synthetic data as well as datasets from previous performances.

@article{hill2021sound,
author={hill, adam j. and mulder, johannes and burton, jon and kok, marcel and lawrence, michael},
journal={journal of the audio engineering society},
title={sound level monitoring at live events, part 1--live dynamic range},
year={2021},
volume={69},
number={11},
pages={782-792},
doi={https://doi.org/10.17743/jaes.2021.0050},
month={november},} @article{hill2021sound,
author={hill, adam j. and mulder, johannes and burton, jon and kok, marcel and lawrence, michael},
journal={journal of the audio engineering society},
title={sound level monitoring at live events, part 1--live dynamic range},
year={2021},
volume={69},
number={11},
pages={782-792},
doi={https://doi.org/10.17743/jaes.2021.0050},
month={november},
abstract={musical dynamics are often central within pieces of music and are therefore likely to be fundamental to the live event listening experience. while metrics exist in broadcasting and recording to quantify dynamics, such measures work on high-resolution data. live event sound level monitoring data is typically low-resolution (logged at one second intervals or less), which necessitates bespoke musical dynamics quantification. live dynamic range (ldr) is presented and validated here to serve this purpose, wheremeasurement data is conditioned to remove song breaks and sound level regulation-imposed adjustments to extract the true musical dynamics from a live performance. results show consistent objective performance of the algorithm, as tested on synthetic data as well as datasets from previous performances.},}

TY - paper
TI - Sound Level Monitoring at Live Events, Part 1--Live Dynamic Range
SP - 782 EP - 792
AU - Hill, Adam J.
AU - Mulder, Johannes
AU - Burton, Jon
AU - Kok, Marcel
AU - Lawrence, Michael
PY - 2021
JO - Journal of the Audio Engineering Society
IS - 11
VO - 69
VL - 69
Y1 - November 2021 TY - paper
TI - Sound Level Monitoring at Live Events, Part 1--Live Dynamic Range
SP - 782 EP - 792
AU - Hill, Adam J.
AU - Mulder, Johannes
AU - Burton, Jon
AU - Kok, Marcel
AU - Lawrence, Michael
PY - 2021
JO - Journal of the Audio Engineering Society
IS - 11
VO - 69
VL - 69
Y1 - November 2021
AB - Musical dynamics are often central within pieces of music and are therefore likely to be fundamental to the live event listening experience. While metrics exist in broadcasting and recording to quantify dynamics, such measures work on high-resolution data. Live event sound level monitoring data is typically low-resolution (logged at one second intervals or less), which necessitates bespoke musical dynamics quantification. Live dynamic range (LDR) is presented and validated here to serve this purpose, wheremeasurement data is conditioned to remove song breaks and sound level regulation-imposed adjustments to extract the true musical dynamics from a live performance. Results show consistent objective performance of the algorithm, as tested on synthetic data as well as datasets from previous performances.