Drum Pattern Humanization Using a Recursive Bayesian Framework

Stables R, Athwal C, Cade R. Drum Pattern Humanization Using a Recursive Bayesian Framework. In: AES Convention 133. Audio Engineering Society; 2012. Paper 8763. Available from: https://aes.org/publications/elibrary-page/?id=16505

@inproceedings{Stables2012_16505,
  author = {Stables, Ryan and Athwal, Cham and Cade, Rob},
  title = {{Drum Pattern Humanization Using a Recursive Bayesian Framework}},
  booktitle = {AES Convention 133},
  note = {Paper 8763},
  year = {2012},
  month = oct,
  publisher = {Audio Engineering Society},
  url = {https://aes.org/publications/elibrary-page/?id=16505}
}

TY  - CPAPER
TI  - Drum Pattern Humanization Using a Recursive Bayesian Framework
AU  - Stables, Ryan
AU  - Athwal, Cham
AU  - Cade, Rob
T2  - AES Convention 133
M1  - Paper 8763
PY  - 2012
DA  - 2012/10/06
UR  - https://aes.org/publications/elibrary-page/?id=16505
PB  - Audio Engineering Society
LA  - en
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.
ER  -