Automatic Loudspeaker Room Equalization Based On Sound Field Estimation with Artificial Intelligence Models

Celestinos A, Li Y, Chin Lopez VM. Automatic Loudspeaker Room Equalization Based On Sound Field Estimation with Artificial Intelligence Models. In: AES Convention 151. Audio Engineering Society; 2021. Paper 10520. Available from: https://aes.org/publications/elibrary-page/?id=21484

@inproceedings{Celestinos2021_21484,
  author = {Celestinos, Adrian and Li, Yuan and Chin Lopez, Victor Manuel},
  title = {{Automatic Loudspeaker Room Equalization Based On Sound Field Estimation with Artificial Intelligence Models}},
  booktitle = {AES Convention 151},
  note = {Paper 10520},
  year = {2021},
  month = oct,
  publisher = {Audio Engineering Society},
  url = {https://aes.org/publications/elibrary-page/?id=21484}
}

TY  - CPAPER
TI  - Automatic Loudspeaker Room Equalization Based On Sound Field Estimation with Artificial Intelligence Models
AU  - Celestinos, Adrian
AU  - Li, Yuan
AU  - Chin Lopez, Victor Manuel
T2  - AES Convention 151
M1  - Paper 10520
PY  - 2021
DA  - 2021/10/06
UR  - https://aes.org/publications/elibrary-page/?id=21484
PB  - Audio Engineering Society
LA  - en
AB  - In-room loudspeaker equalization requires a significant amount of microphone positions in order to characterize the sound field in the room. This can be a cumbersome task for the user. This paper proposes the use of artificial intelligence to automatically estimate and equalize, without user interaction, the in-room response. To learn the relationship between loudspeaker near-field response and total sound power, or energy average over the listening area, a neural network was trained using room measurement data. Loudspeaker near-field SPL at discrete frequencies was the input data to the neural network. The approach has been tested in a subwoofer, a full-range loudspeaker, and a TV. Results showed that the in-room sound field can be estimated within 1–2 dB average standard deviation.
ER  -