While previous research on spatial sound perception investigated the physical mechanisms producing the most relevant elevation cues, how spectral notches are generated and related to the individual morphology of the human pinna is still a topic of debate. Correctly modeling these important elevation cues, and in particular the lowest frequency notches, is an essential step for individualizing Head-Related Transfer Functions (HRTFs). In this paper we propose a simple computational model able to predict the center frequencies of pinna notches from ear meshes. We apply such a model to a highly controlled HRTF dataset built with the specific purpose of understanding the contribution of the pinna to the HRTF. Results show that the computational model is able to approximate the lowest frequency notch with improved accuracy with respect to other state-of-the-art methods. By contrast, the model fails to predict higher-order pinna notches correctly. The proposed approximation supplements understanding of the morphology involved in generating spectral notches in experimental HRTFs.@en

Systemic risks are potentially harmful events, that could severely disrupt an entire industry or economy. Examples include the bankruptcy of keystone companies and biosecurity incursions. According to the United Nations, detecting and managing systemic risk represents one of the main challenges of the 21st century. Due to increasing complexity and interconnectedness of today's social-technological-biophysical systems, stakeholders relying on individual disciplines to systemic risk detection, or a combination of disciplines that are not well coordinated, will fail to promptly identify key early warning signals of threats. Our paper argues that transdisciplinary approaches are required to make comprehensive and integrative assessments of complex systems. To support stakeholders undertaking such assessments, we propose a framework that will assist them in: (1) better understanding their system and the risks to which it is exposed; (2) selecting complementary disciplines, theories and methods that are relevant to the system and risks in question; and (3) integrating knowledge from these different disciplines to detect a wide range of early warning signals of systemic risk. The framework can be used as a foundation to build transdisciplinary approaches to risk detection.@en