Binaural Sound Localization Based on Reverberation Weighting and Generalized Parametric Mapping

Cheng Pang*, Hong Liu, Jie Zhang, Xiaofei Li

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

30 Citations (Scopus)
48 Downloads (Pure)


Binaural sound source localization is an important technique for speech enhancement, video conferencing, and human-robot interaction, etc. However, in realistic scenarios, the reverberation and environmental noise would degrade the precision of sound direction estimation. Therefore, reliable sound localization is essential to practical applications. To deal with these disturbances, this paper presents a novel binaural sound source localization approach based on reverberation weighting and generalized parametric mapping. First, the reverberation weighting as a preprocessing stage, is used to separately suppress the early and late reverberation, while preserving interaural cues. Then, two binaural cues, i.e., interaural time and intensity differences, are extracted from the frequency-domain representations of dereverberated binaural signals for the online localization. Their corresponding templates are established using the training data. Furthermore, the generalized parametric mapping is proposed to build a generalized parametric model for describing relationships between azimuth and binaural cues analytically. Finally, a two-step sound localization process is introduced to refine azimuth estimation based on the generalized parametric model and template matching. Experiments in both simulated and real scenarios validate that the proposed method can achieve better localization performance compared to state-of-the-art methods.

Original languageEnglish
Article number7926345
Pages (from-to)1618-1632
Number of pages15
JournalIEEE - ACM Transactions on Audio, Speech, and Language Processing
Issue number8
Publication statusPublished - 2017

Bibliographical note

Accepted Author Manuscript


  • Binaural localization
  • reverberation weighting
  • generalized parametric mapping
  • template matching


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