Active Sound Quality Control Based on Individual Subjective Preference

Murai K, Ishimitsu S, Aramaki Y, Shibatani N, Takagi T, Yoshida K, Suzuki K, Chino T. Active Sound Quality Control Based on Individual Subjective Preference. In: AES Conference: 2017 AES International Conference on Automotive Audio. Audio Engineering Society; 2017. Paper P2-4. Available from: https://aes.org/publications/elibrary-page/?id=19195

@inproceedings{Murai2017_19195,
  author = {Murai, Kenta and Ishimitsu, Shunsuke and Aramaki, Yoshihiro and Shibatani, Naoaki and Takagi, Toshihisa and Yoshida, Kazuki and Suzuki, Kenta and Chino, Takanori},
  title = {{Active Sound Quality Control Based on Individual Subjective Preference}},
  booktitle = {AES Conference: 2017 AES International Conference on Automotive Audio},
  note = {Paper P2-4},
  year = {2017},
  month = aug,
  publisher = {Audio Engineering Society},
  url = {https://aes.org/publications/elibrary-page/?id=19195}
}

TY  - CPAPER
TI  - Active Sound Quality Control Based on Individual Subjective Preference
AU  - Murai, Kenta
AU  - Ishimitsu, Shunsuke
AU  - Aramaki, Yoshihiro
AU  - Shibatani, Naoaki
AU  - Takagi, Toshihisa
AU  - Yoshida, Kazuki
AU  - Suzuki, Kenta
AU  - Chino, Takanori
T2  - AES Conference: 2017 AES International Conference on Automotive Audio
M1  - Paper P2-4
PY  - 2017
DA  - 2017/08/06
UR  - https://aes.org/publications/elibrary-page/?id=19195
PB  - Audio Engineering Society
LA  - en
AB  - In recent years, the engine-sound control method has shifted from noise reduction to sound design. Therefore, we have proposed a method to design the engine sound using active sound quality control (ASQC) based on ANC technology. Specifically, we propose an algorithm for amplifying and reducing the engine-specific order components. In addition, the auditory impressions of engine sound controlled by ASQC were investigated using psychoacoustic measurements. The results indicated that the control corresponding to the individual is important for improvements in auditory impressions. So, ASQC was developed to adjust to individual preferences. The individual preferences were connected to each driver’s driving pattern using deep learning. Thus, we developed an ASQC system, which enables the automatic generation of individual sound preferences.
ER  -