Direction of arrival estimation based on transfer function learning using autoencoder network

Wang Y, Wu X, Qu T. Direction of arrival estimation based on transfer function learning using autoencoder network. In: AES Convention 148. Audio Engineering Society; 2020. Paper 10370. Available from: https://aes.org/publications/elibrary-page/?id=20787

@inproceedings{Wang2020_20787,
  author = {Wang, Yiwen and Wu, Xihong and Qu, Tianshu},
  title = {{Direction of arrival estimation based on transfer function learning using autoencoder network}},
  booktitle = {AES Convention 148},
  note = {Paper 10370},
  year = {2020},
  month = may,
  publisher = {Audio Engineering Society},
  url = {https://aes.org/publications/elibrary-page/?id=20787}
}

TY  - CPAPER
TI  - Direction of arrival estimation based on transfer function learning using autoencoder network
AU  - Wang, Yiwen
AU  - Wu, Xihong
AU  - Qu, Tianshu
T2  - AES Convention 148
M1  - Paper 10370
PY  - 2020
DA  - 2020/05/06
UR  - https://aes.org/publications/elibrary-page/?id=20787
PB  - Audio Engineering Society
LA  - en
AB  - Direction-of-arrival (DOA) estimation based on microphone arrays has been a hot research topic in recent years. Transfer function (TF) based DOA method performs well because it considers both time difference and intensity difference. However, obtaining transfer function is a difficult task and transfer function based method is susceptible to noise. In this paper, an autoencoder network structure is proposed for DOA estimation task. The network is used to learn the characteristics of the transfer function, which considers both time difference information and intensity difference information for DOA estimation. The proposed unsupervised training method helps minimize the burden for labeling training data. The evaluation experiments show that our method performs better than TF-based method in the noisy environment.
ER  -