This article proposes a system for object-based six-degrees-of-freedom (6DoF) rendering of spatial sound scenes that are captured using a distributed arrangement of multiple Ambisonic receivers. The approach is based on first identifying and tracking the positions of sound sources within the scene, followed by the isolation of their signals through the use of beamformers. These sound objects are subsequently spatialized over the target playback setup, with respect to both the head orientation...

Ambisonics has enjoyed a recent resurgence in popularity due to virtual reality applications. Low order Ambisonic reproduction is inherently inaccurate at high frequencies, which causes poor timbre and height localisation. Diffuse-Field Equalisation (DFE), the theory of removing direction-independent frequency response, is applied to binaural (over headphones) Ambisonic rendering to address high-frequency reproduction. DFE of Ambisonics is evaluated by comparing binaural Ambisonic rendering to direct convolution via head-related impulse responses (HRIRs) in three ways: spectral difference, predicted sagittal plane localisation and perceptual listening tests on timbre. Results show DFE successfully improves frequency reproduction of binaural Ambisonic rendering for the majority of sound source locations, as well as the limitations of the technique, and set the basis for further research in the field.

Ambisonics is a spatial audio technique appropriate for dynamic binaural rendering due to its sound field rotation and transformation capabilities, which has made it popular for virtual reality applications. An issue with low-order Ambisonics is that interaural level differences (ILDs) are often reproduced with lower values when compared to head-related impulse responses (HRIRs), which reduces lateralization and spaciousness. This paper introduces a method of Ambisonic ILD Optimization (AIO), a pre-processing technique to bring the ILDs produced by virtual loudspeaker binaural Ambisonic rendering closer to those of HRIRs. AIO is evaluated objectively for Ambisonic orders up to fifth order versus a reference dataset of HRIRs for all locations on the sphere via estimated ILD and spectral difference, and perceptually through listening tests using both simple and complex scenes. Results conclude AIO produces an overall improvement for all tested orders of Ambisonics, though the benefits are greatest at first and second order.

Acoustically transparent head-worn devices are a key component of auditory augmented reality systems, in which both real and virtual sound sources are presented to a listener simultaneously. Head-worn devices can exhibit high transparency simply through their physical design but in practice will always obstruct the sound field to some extent. In this study, a method for predicting the perceptual transparency of head-worn devices is presented using numerical analysis of device measurements,...

Spatial room impulse responses (SRIRs) capture room acoustics with directional information. SRIRs measured in coupled rooms and spaces with non-uniform absorption distribution may exhibit anisotropic reverberation decays and multiple decay slopes. However, noisy measurements with low signal-to-noise ratios pose issues in analysis and reproduction in practice. This paper presents a method for resynthesis of the late decay of anisotropic SRIRs, effectively removing noise from SRIR measurements....

Although the difference between the fast Fourier transforms of two audio signals is often used as a basic measure of predicting perceived colouration, these signal measures do not provide information on how relevant the results are from a perceptual point of view. This paper presents a perceptually motivated loudness calculation for predicting the colouration between binaural signals which incorporates equal loudness frequency contouring, relative subjective loudness weighting, cochlea frequency modelling, and an iterative normalisation of input signals. The validation compares the presented model to three other colouration calculations in two ways: using test signals designed to evaluate specific elements of the model, and against the results of a listening test on degraded binaural audio signals. Results demonstrate the presented model is appropriate for predicting the colouration between binaural signals.