101 Mixes: A Statistical Analysis of Mix-Variation in a Dataset of Multi-Track Music Mixes

Wilson A, Fazenda B. 101 Mixes: A Statistical Analysis of Mix-Variation in a Dataset of Multi-Track Music Mixes. In: AES Convention 139. Audio Engineering Society; 2015. Paper 9398. Available from: https://aes.org/publications/elibrary-page/?id=17955

@inproceedings{Wilson2015_17955,
  author = {Wilson, Alex and Fazenda, Bruno},
  title = {{101 Mixes: A Statistical Analysis of Mix-Variation in a Dataset of Multi-Track Music Mixes}},
  booktitle = {AES Convention 139},
  note = {Paper 9398},
  year = {2015},
  month = oct,
  publisher = {Audio Engineering Society},
  url = {https://aes.org/publications/elibrary-page/?id=17955}
}

TY  - CPAPER
TI  - 101 Mixes: A Statistical Analysis of Mix-Variation in a Dataset of Multi-Track Music Mixes
AU  - Wilson, Alex
AU  - Fazenda, Bruno
T2  - AES Convention 139
M1  - Paper 9398
PY  - 2015
DA  - 2015/10/06
UR  - https://aes.org/publications/elibrary-page/?id=17955
PB  - Audio Engineering Society
LA  - en
AB  - The act of mix-engineering is a complex combination of creative and technical processes; analysis is often performed by studying the techniques of a few expert practitioners qualitatively. We propose to study the actions of a large group of mix-engineers of varying experience, introducing quantitative methodology to investigate mix-variation and the perception of quality. This paper describes the analysis of a dataset containing 101 alternate mixes generated by human mixers as part of an on-line mix competition. A varied selection of audio signal features is obtained from each mix and subsequent principal component analysis reveals four prominent dimensions of variation: dynamics, treble, width, and bass. An ordinal logistic regression model suggests that the ranking of each mix in the competition was significantly influenced by these four dimensions. The implications for the design of intelligent music production systems are discussed.
ER  -