Perceptually Relevant Models for Articulation in Synthesised Drum Patterns

Stables R, Bullock J, Williams I. Perceptually Relevant Models for Articulation in Synthesised Drum Patterns. In: AES Convention 131. Audio Engineering Society; 2011. Paper 8478. Available from: https://aes.org/publications/elibrary-page/?id=16004

@inproceedings{Stables2011_16004,
  author = {Stables, Ryan and Bullock, Jamie and Williams, Ian},
  title = {{Perceptually Relevant Models for Articulation in Synthesised Drum Patterns}},
  booktitle = {AES Convention 131},
  note = {Paper 8478},
  year = {2011},
  month = oct,
  publisher = {Audio Engineering Society},
  url = {https://aes.org/publications/elibrary-page/?id=16004}
}

TY  - CPAPER
TI  - Perceptually Relevant Models for Articulation in Synthesised Drum Patterns
AU  - Stables, Ryan
AU  - Bullock, Jamie
AU  - Williams, Ian
T2  - AES Convention 131
M1  - Paper 8478
PY  - 2011
DA  - 2011/10/06
UR  - https://aes.org/publications/elibrary-page/?id=16004
PB  - Audio Engineering Society
LA  - en
AB  - In this study, we evaluate current techniques for drum pattern humanisation and suggest new methods using a probabilistic model. Our statistical analysis shows that both deviations from a fixed grid and corresponding amplitude values of drum patterns can have non-Gaussian distributions with underlying temporal structures. We plot distributions and probability matrices of sequences played by humans in order to demonstrate this. A new method for humanisation with structural preservation is proposed, using a Markov Chain and an Empirical Cumulative Distribution Function (ECDF) in order to weight pseudorandom variables. Finally we demonstrate the perceptual relevance of these methods using paired listening tests.
ER  -