An Intelligent Interface for Drum Pattern Variation and Comparative Evaluation of Algorithms

Vogl R, Leimeister M, Nuanáin CÓ, Jordà S, Hlatky M, Knees P. An Intelligent Interface for Drum Pattern Variation and Comparative Evaluation of Algorithms. J Audio Eng Soc. 2016;64(7/8):503-513. doi:10.17743/jaes.2016.0016

@article{Vogl2016_18336,
  author = {Vogl, Richard and Leimeister, Matthias and Nuanáin, Carthach Ó and Jordà, Sergi and Hlatky, Michael and Knees, Peter},
  title = {{An Intelligent Interface for Drum Pattern Variation and Comparative Evaluation of Algorithms}},
  journal = {Journal of the Audio Engineering Society},
  volume = {64},
  number = {7/8},
  pages = {503--513},
  year = {2016},
  month = jul,
  publisher = {Audio Engineering Society},
  doi = {10.17743/jaes.2016.0016},
  url = {https://doi.org/10.17743/jaes.2016.0016}
}

TY  - JOUR
TI  - An Intelligent Interface for Drum Pattern Variation and Comparative Evaluation of Algorithms
AU  - Vogl, Richard
AU  - Leimeister, Matthias
AU  - Nuanáin, Carthach Ó
AU  - Jordà, Sergi
AU  - Hlatky, Michael
AU  - Knees, Peter
T2  - Journal of the Audio Engineering Society
J2  - J. Audio Eng. Soc.
VL  - 64
IS  - 7/8
SP  - 503
EP  - 513
PY  - 2016
DA  - 2016/07/06
DO  - 10.17743/jaes.2016.0016
UR  - https://doi.org/10.17743/jaes.2016.0016
PB  - Audio Engineering Society
LA  - en
AB  - Drum tracks for electronic dance music are a central and style-defining element. But creating them can be a cumbersome task because of a lack of appropriate tools and input devices. The authors created a tool that supports musicians in an intuitive way for creating variations of drum patterns or finding inspiration for new patterns. Starting with a basic seed pattern provided by the user, a list of variations with varying degrees of similarity to the seed is generated. The variations are created using one of the three algorithms: a similarity-based lookup method using a rhythm pattern database, a generative approach based on a stochastic neural network, and a genetic algorithm using similarity measures as target function. Expert users in electronic music production evaluated aspects of the prototype and algorithms. In addition, a web-based survey was performed to assess perceptual properties of the variations in comparison to baseline patterns created by a human expert. The study shows that the algorithms produce musical and interesting variations and that the different algorithms have their strengths in different areas.
ER  -