Exploring Preference for Multitrack Mixes Using Statistical Analysis of MIR and Textual Features

Colonel J, Reiss JD. Exploring Preference for Multitrack Mixes Using Statistical Analysis of MIR and Textual Features. In: AES Convention 147. Audio Engineering Society; 2019. Paper 526. Available from: https://aes.org/publications/elibrary-page/?id=20549

@inproceedings{Colonel2019_20549,
  author = {Colonel, Joseph and Reiss, Joshua D.},
  title = {{Exploring Preference for Multitrack Mixes Using Statistical Analysis of MIR and Textual Features}},
  booktitle = {AES Convention 147},
  note = {Paper 526},
  year = {2019},
  month = oct,
  publisher = {Audio Engineering Society},
  url = {https://aes.org/publications/elibrary-page/?id=20549}
}

TY  - CPAPER
TI  - Exploring Preference for Multitrack Mixes Using Statistical Analysis of MIR and Textual Features
AU  - Colonel, Joseph
AU  - Reiss, Joshua D.
T2  - AES Convention 147
M1  - Paper 526
PY  - 2019
DA  - 2019/10/06
UR  - https://aes.org/publications/elibrary-page/?id=20549
PB  - Audio Engineering Society
LA  - en
AB  - We investigate listener preference in multitrack music production using the Mix Evaluation Dataset, comprised of 184 mixes across 19 songs. Features are extracted from verses and choruses of stereo mixdowns. Each observation is associated with an average listener preference rating and standard deviation of preference ratings. Principal component analysis is performed to analyze how mixes vary within the feature space. We demonstrate that virtually no correlation is found between the embedded features and either average preference or standard deviation of preference. We instead propose using principal component projections as a semantic embedding space by associating each observation with listener comments from the Mix Evaluation Dataset. Initial results disagree with simple descriptions such as “width” or “loudness” for principal component axes.
ER  -