This paper studies the relationship between head-related transfer functions (HRTFs) and pinna reflection patterns in the frontal hemispace. A pre-processed database of HRTFs allows extraction of up to three spectral notches from each response taken in the median sagittal plane. Ray-tracing analysis performed on the obtained notches' central frequencies is compared with a set of possible reflection surfaces directly recognizeable from the corresponding pinna picture. Results of such analysis are discussed in terms of the reflection coefficient sign, which is found to be most likely negative. Based on this finding, a model for real-time HRTF synthesis that allows to control separately the evolution of different acoustic phenomena such as head diffraction, ear resonances, and reflections is proposed through the design of distinct filter blocks. Parameters to be fed to the model are derived either from analysis or from specific anthropometric features of the subject. Finally, objective evaluations of reconstructed HRTFs in the chosen spatial range are performed through spectral distortion measurements.

Nowadays, Virtual and Augmented Reality technologies play a supportive role in many research fields. In cultural heritage, various examples are available, including storytelling and narratives, where they can provide immersive and enhanced experiences to visitors and tourists, especially for entertainment and educational purposes. This review aims to investigate the opportunities that soundscape design and advanced sonic interactions in virtual and augmented environments can bring to cultural heritage sites and museums in terms of presence, emotional content, and cultural dissemination. Nineteen-two papers have been identified through the PRISMA methodology, and a promising positive effect of sonic interaction on user experience in a virtual environment can be observed in various studies, notwithstanding a general lack of specific contributions on the use of sound rendering and audio spatialisation for improving such experiences. Moreover, this work identifies the main involved research areas and discusses the state-of-the-art best practices and case studies where sonic interactions may assume a central role. The final part suggests possible future directions and applications for more engaging and immersive storytelling in the cultural heritage domain.

Humans estimate sound-source directions by combining prior beliefs with sensory evidence. Prior beliefs represent statistical knowledge about the environment, and the sensory evidence consists of auditory features such as interaural disparities and monaural spectral shapes. Models of directional sound localization often impose constraints on the contribution of these features to either the horizontal or vertical dimension. Instead, we propose a Bayesian model that flexibly incorporates each feature according to its spatial precision and integrates prior beliefs in the inference process. The model estimates the direction of a single, broadband, stationary sound source presented to a static human listener in an anechoic environment. We simplified interaural features to be broadband and compared two model variants, each considering a different type of monaural spectral features: magnitude profiles and gradient profiles. Both model variants were fitted to the baseline performance of five listeners and evaluated on the effects of localizing with non-individual head-related transfer functions (HRTFs) and sounds with rippled spectrum. We found that the variant equipped with spectral gradient profiles outperformed other localization models. The proposed model appears particularly useful for the evaluation of HRTFs and may serve as a basis for future extensions towards modeling dynamic listening conditions.

Machine learning (ML) has become pervasive in various research fields, including binaural synthesis personalization, which is crucial for sound in immersive virtual environments. Researchers have mainly addressed this topic by estimating the individual head-related transfer function (HRTF). HRTFs are utilized to render audio signals at specific spatial positions, thereby simulating real-world sound wave interactions with the human body. As such, an HRTF that is compliant with individual characteristics enhances the realism of the binaural simulation. This survey systematically examines the HRTF individualization works based on ML proposed in the literature. The analyzed works are organized according to the processing steps involved in the ML workflow, including the employed dataset, input and output types, data preprocessing operations, ML models, and model evaluation. In addition to categorizing the works of the existing literature, this survey discusses their achievements, identifies their limitations, and outlines aspects that require further investigation at the crossroads of research communities in acoustics, audio signal processing, and machine learning.