Open AccessHead‐related transfer function–reserved time‐frequency masking for robust binaural sound source localization
Author(s) -
Liu Hong,
Yuan Peipei,
Yang Bing,
Yang Ge,
Chen Yang
Publication year - 2022
Publication title -
caai transactions on intelligence technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.613
H-Index - 15
ISSN - 2468-2322
DOI - 10.1049/cit2.12010
Subject(s) - binaural recording , head related transfer function , computer science , sound localization , speech recognition , transfer function , acoustic source localization , masking (illustration) , acoustics , convolutional neural network , artificial intelligence , sound (geography) , engineering , physics , art , electrical engineering , visual arts
Various time‐frequency (T‐F) masks are being applied to sound source localization tasks. Moreover, deep learning has dramatically advanced T‐F mask estimation. However, existing masks are usually designed for speech separation tasks and are suitable only for single‐channel signals. A novel complex‐valued T‐F mask is proposed that reserves the head‐related transfer function (HRTF), customized for binaural sound source localization. In addition, because the convolutional neural network that is exploited to estimate the proposed mask takes binaural spectral information as the input and output, accurate binaural cues can be preserved. Compared with conventional T‐F masks that emphasize single speech source–dominated T‐F units, HRTF‐reserved masks eliminate the speech component while keeping the direct propagation path. Thus, the estimated HRTF is capable of extracting more reliable localization features for the final direction of arrival estimation. Hence, binaural sound source localization guided by the proposed T‐F mask is robust under noisy and reverberant acoustic environments. The experimental results demonstrate that the new T‐F mask is superior to conventional T‐F masks and lead to the better performance of sound source localization in adverse environments.