Rapid Learning of Subjective Preference in Equalization

Sabin A, Pardo B. Rapid Learning of Subjective Preference in Equalization. In: AES Convention 125. Audio Engineering Society; 2008.pp. 7581:1-. Available from: https://aes.org/publications/elibrary-page/?id=14733

@inproceedings{Sabin2008_14733,
  author = {Sabin, Andrew and Pardo, Bryan},
  title = {{Rapid Learning of Subjective Preference in Equalization}},
  booktitle = {AES Convention 125},
  pages = {7581:1-},
  year = {2008},
  month = oct,
  publisher = {Audio Engineering Society},
  url = {https://aes.org/publications/elibrary-page/?id=14733}
}

TY  - CPAPER
TI  - Rapid Learning of Subjective Preference in Equalization
AU  - Sabin, Andrew
AU  - Pardo, Bryan
T2  - AES Convention 125
SP  - 1
M1  - Paper 7581
PY  - 2008
DA  - 2008/10/06
UR  - https://aes.org/publications/elibrary-page/?id=14733
PB  - Audio Engineering Society
LA  - en
AB  - We describe and test an algorithm to rapidly learn a listener’s desired equalization curve. First, a sound is modified by a series of equalization curves. After each modification, the listener indicates how well the current sound exemplifies a target sound descriptor (e.g., “warm”). After rating, a weighting function is computed where the weight of each channel (frequency band) is proportional to the slope of the regression line between listener responses and within-channel gain. Listeners report that sounds generated using this function capture their intended meaning of the descriptor. Machine ratings generated by computing the similarity of a given curve to the weighting function are highly correlated to listener responses, and asymptotic performance is reached after only ~25 listener ratings.
ER  -