Localization of Direct Source and Early Reflections Using HOA Processing and DNN Model

Gao S, Wu X, Qu T. Localization of Direct Source and Early Reflections Using HOA Processing and DNN Model. In: AES Convention 152. Audio Engineering Society; 2022. Paper 10560. Available from: https://aes.org/publications/elibrary-page/?id=21673

@inproceedings{Gao2022_21673,
  author = {Gao, Shan and Wu, Xihong and Qu, Tianshu},
  title = {{Localization of Direct Source and Early Reflections Using HOA Processing and DNN Model}},
  booktitle = {AES Convention 152},
  note = {Paper 10560},
  year = {2022},
  month = may,
  publisher = {Audio Engineering Society},
  url = {https://aes.org/publications/elibrary-page/?id=21673}
}

TY  - CPAPER
TI  - Localization of Direct Source and Early Reflections Using HOA Processing and DNN Model
AU  - Gao, Shan
AU  - Wu, Xihong
AU  - Qu, Tianshu
T2  - AES Convention 152
M1  - Paper 10560
PY  - 2022
DA  - 2022/05/06
UR  - https://aes.org/publications/elibrary-page/?id=21673
PB  - Audio Engineering Society
LA  - en
AB  - This paper proposes a novel direct source and first-order reflections localization method by integrating the high order Ambisonics (HOA) algorithm and deep neural network. We use the covariance matrix of HOA signals in the time domain as the input feature of the network, which contains precise spatial information of the sound sources under reverberant scenarios. Besides, we use the deconvolution-based neural network (DCNN) for the spatial pseudo-spectrum (SPS) reconstruction, based on which the spatial relationship between elevation and azimuth can be depicted. Considering that the first-order reflections of the sound source also contain spatial directivity like the direct source, we treat both of them as the sources in the learning process. We have carried out a series of experiments based on simulated and measured data under different reverberant scenarios, which prove the effectiveness and accuracy of the proposed DCNN model.
ER  -