Variation in Multitrack Mixes: Analysis of Low-level Audio Signal Features

Wilson A, Fazenda B. Variation in Multitrack Mixes: Analysis of Low-level Audio Signal Features. J Audio Eng Soc. 2016;64(7/8):466-473. doi:10.17743/jaes.2016.0029

@article{Wilson2016_18332,
  author = {Wilson, Alex and Fazenda, Bruno},
  title = {{Variation in Multitrack Mixes: Analysis of Low-level Audio Signal Features}},
  journal = {Journal of the Audio Engineering Society},
  volume = {64},
  number = {7/8},
  pages = {466--473},
  url = {https://doi.org/10.17743/jaes.2016.0029},
  doi = {10.17743/jaes.2016.0029},
  year = {2016},
  month = jul,
  publisher = {Audio Engineering Society}
}

TY  - JOUR
TI  - Variation in Multitrack Mixes: Analysis of Low-level Audio Signal Features
AU  - Wilson, Alex
AU  - Fazenda, Bruno
T2  - Journal of the Audio Engineering Society
J2  - J. Audio Eng. Soc.
VL  - 64
IS  - 7/8
SP  - 466
EP  - 473
PY  - 2016
DA  - 2016/07/06
DO  - 10.17743/jaes.2016.0029
UR  - https://doi.org/10.17743/jaes.2016.0029
PB  - Audio Engineering Society
LA  - en
AB  - Understanding how a human mixing engineer functions is necessary for the design of intelligent music production tools. The goal of such tools is to generate mixes that could realistically have been created by a human mix-engineer. This paper presents an analysis of 1501 mixes, over 10 different songs, created by mix-engineers. The number of mixes of each song ranged from 97 to 373. A variety of objective signal features were extracted and principal component analysis was performed revealing four dimensions of mix-variation, which can be described as amplitude, brightness, bass, and width. Feature distribution suggests multimodal behavior dominated by one specific mode. This distribution appears to be independent of the choice of song, but with variation in modal parameters. This is then used to obtain general trends and tolerance bounds for these features. The results presented here are useful as parametric guidance for intelligent music production systems. This provides insight into the creative decision making processes of mix-engineers.
ER  -