This research aims at contributing to the emergence of future cognitive-based audio processing methods for acoustic environment recognition. To achieve this, patterns of invariability are being identified along databases of morphological and acoustical descriptors of the listeners’ external anatomy.
A dataset of near-distance HRTFs has been constructed as part of this project. The dataset is publicly available for download at cesardsalvador.github.io/download.html.
This research also aims at establishing mathematical correspondences between the acoustical patterns of invariability and the statistical relations of connectivity in the auditory brain. We are reviewing recent models of structural (anatomical links) and functional (statistical association) connectivity that occur in the bottom-up (stimulus-driven) and top-down (task-oriented) neural pathways of the human auditory brain when sound identification and localization tasks are performed. By interpreting the state-of-the-art in auditory brain modeling from a signal processing perspective, recent findings in the fields of neurobiology, cognitive science, complex brain networks, and mathematical neuroscience are being integrated into a comprehensive framework for acoustic environment recognition.
This project is being suported by a Grant-in-Aid for Young Scientists (B) from the Japan Society for the Promotion of Science (JSPS), under Grant JP17K12708, 2017-2018.