Deep Learning for Loudspeaker Digital Twin Creation

Louise B, Kerimovs T, Schlecht SJ. Deep Learning for Loudspeaker Digital Twin Creation. In: AES Convention 154. Audio Engineering Society; 2023. Paper 10642. Available from: https://aes.org/publications/elibrary-page/?id=22049

@inproceedings{Louise2023_22049,
  author = {Louise, Bryn and Kerimovs, Teodors and Schlecht, Sebastian J.},
  title = {{Deep Learning for Loudspeaker Digital Twin Creation}},
  booktitle = {AES Convention 154},
  note = {Paper 10642},
  year = {2023},
  month = may,
  publisher = {Audio Engineering Society},
  url = {https://aes.org/publications/elibrary-page/?id=22049}
}

TY  - CPAPER
TI  - Deep Learning for Loudspeaker Digital Twin Creation
AU  - Louise, Bryn
AU  - Kerimovs, Teodors
AU  - Schlecht, Sebastian J.
T2  - AES Convention 154
M1  - Paper 10642
PY  - 2023
DA  - 2023/05/06
UR  - https://aes.org/publications/elibrary-page/?id=22049
PB  - Audio Engineering Society
LA  - en
AB  - Several studies have used deep learning methods to create digital twins of amps, speakers, and effects pedals. This paper presents a novel method for creating a digital twin of a physical loudspeaker with stereo output. Two neural network architectures are considered: a Recurrent Neural Network (RNN) and a WaveNet-style Convolutional Neural Network (CNN). The models were tested on two datasets containing speech and music, respectively. The method of recording and preprocessing the target audio data addresses the challenge of lacking a direct output line to digitize the effect of nonlinear circuits. Both model architectures successfully create a digital twin of the loudspeaker with no direct output line and stereo audio. The RNN model achieved the best result on the music dataset, while the WaveNet model achieved the best result on the speech dataset.
ER  -