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R. Stables, C. Athwal, and R. Cade, "Drum Pattern Humanization Using a Recursive Bayesian Framework," Paper 8763, (2012 October.). doi: R. Stables, C. Athwal, and R. Cade, "Drum Pattern Humanization Using a Recursive Bayesian Framework," Paper 8763, (2012 October.). doi:
Abstract: In this study we discuss some of the limitations of Gaussian humanization and consider ways in which the articulation patterns exhibited by percussionists can be emulated using a probabilistic model. Prior and likelihood functions are derived from a dataset of professional drummers to create a series of empirical distributions. These are then used to independently modulate the onset locations and amplitudes of a quantized sequence, using a recursive Bayesian framework. Finally, we evaluate the performance of the model against sequences created with a Gaussian humanizer and sequences created with a Hidden Markov Model (HMM) using paired listening tests. We are able to demonstrate that probabilistic models perform better than instantaneous Gaussian models, when evaluated using a 4/4 rock beat at 120 bpm.

@article{stables2012drum,
author={stables, ryan and athwal, cham and cade, rob},
journal={journal of the audio engineering society},
title={drum pattern humanization using a recursive bayesian framework},
year={2012},
volume={},
number={},
pages={},
doi={},
month={october},} @article{stables2012drum,
author={stables, ryan and athwal, cham and cade, rob},
journal={journal of the audio engineering society},
title={drum pattern humanization using a recursive bayesian framework},
year={2012},
volume={},
number={},
pages={},
doi={},
month={october},
abstract={in this study we discuss some of the limitations of gaussian humanization and consider ways in which the articulation patterns exhibited by percussionists can be emulated using a probabilistic model. prior and likelihood functions are derived from a dataset of professional drummers to create a series of empirical distributions. these are then used to independently modulate the onset locations and amplitudes of a quantized sequence, using a recursive bayesian framework. finally, we evaluate the performance of the model against sequences created with a gaussian humanizer and sequences created with a hidden markov model (hmm) using paired listening tests. we are able to demonstrate that probabilistic models perform better than instantaneous gaussian models, when evaluated using a 4/4 rock beat at 120 bpm.},}

TY - paper
TI - Drum Pattern Humanization Using a Recursive Bayesian Framework
SP - EP -
AU - Stables, Ryan
AU - Athwal, Cham
AU - Cade, Rob
PY - 2012
JO - Journal of the Audio Engineering Society
IS -
VO -
VL -
Y1 - October 2012 TY - paper
TI - Drum Pattern Humanization Using a Recursive Bayesian Framework
SP - EP -
AU - Stables, Ryan
AU - Athwal, Cham
AU - Cade, Rob
PY - 2012
JO - Journal of the Audio Engineering Society
IS -
VO -
VL -
Y1 - October 2012
AB - In this study we discuss some of the limitations of Gaussian humanization and consider ways in which the articulation patterns exhibited by percussionists can be emulated using a probabilistic model. Prior and likelihood functions are derived from a dataset of professional drummers to create a series of empirical distributions. These are then used to independently modulate the onset locations and amplitudes of a quantized sequence, using a recursive Bayesian framework. Finally, we evaluate the performance of the model against sequences created with a Gaussian humanizer and sequences created with a Hidden Markov Model (HMM) using paired listening tests. We are able to demonstrate that probabilistic models perform better than instantaneous Gaussian models, when evaluated using a 4/4 rock beat at 120 bpm.