High fidelity spatial audio often performs better when produced using a personalized head-related transfer function (HRTF). However, the direct acquisition of HRTFs is cumbersome and requires specialized equipment. Thus, many personalization methods estimate HRTF features from easily obtained anthropometric features of the pinna, head, and torso. The first HRTF notch frequency (N1) is known to be a dominant feature in elevation localization, and thus a useful feature for HRTF personalization. This paper describes the prediction of N1 frequency from pinna anthropometry using a neural model. Prediction is performed separately on three databases, both simulated and measured, and then by domain mixing in-between the databases. The model successfully predicts N1 frequency for individual databases and by domain mixing between some databases. Prediction errors are better or comparable to those previously reported, showing significant improvement when acquired over a large database and with a larger output range.

Virtual and augmented reality technologies demand high-quality spatial sound recording and playback through headphones. However, achieving high-quality binaural reproduction requires a complex recording system and a large number of microphones. To address this issue, a recent study proposed Bilateral Ambisonics, which involves capturing the sound-field using two low-order microphone arrays located ear-distance apart. We present an analytical analysis of the limitation of a previously suggested head-tracking compensation solution to Bilateral Ambisonics. An alternative approach is proposed to overcome these limits in which the translation operation is band-limited. A subjective evaluation and a listening test are provided and complement the findings of the analytical analysis. Results indicate that in a static scenario, compensating for small lateral head-rotations up to$\pm 30^{\circ }$with good accuracy is possible for microphone arrays of spherical harmonics (SH) order of 1 and for medium rotations of up to$\pm 60^{\circ }$with SH order of 2. When a dynamic scenario is considered, Bilateral Ambisonics of order 2 were comparable to High-order Ambisonics, and Bilateral Ambisonics of order 1 provided performance comparable to third order Ambisonics with MagLS.

Reproducing accurate and perceptually realistic spatial audio for augmented and virtual reality (AR/VR) requires the headphones to have a flat frequency response. This can be achieved by equalizing the headphone transducers’ output given the transfer function between the transducer and the human ear, referred to as Ear Acoustic Response (EAR). EAR is unique to every individual and is a function of the transducer characteristics, the user’s anthropometric features (e.g. ear and head shape) and the interactions between the two. This paper proposes a novel method to infer the EAR given the ear features of any listener using a probabilistic framework and a sub-sample of the population as prior. We introduce an approach to assess the level of personalization achieved and benchmark the improvements delivered by the proposed algorithm relative to a generic solution.

Spherical harmonic (SH) representations of sound fields are usually obtained from microphone arrays with rigid spherical baffles whereby the microphones are distributed over the entire surface of the baffle. We present a method that overcomes the requirement for the baffle to be spherical. Furthermore, the microphones can be placed along a circumferential contour around the baffle. This greatly reduces the required number of microphones for a given spatial resolution compared to conventional spherical arrays. Our method is based on the analytical solution for SH decomposition based on observations along the equator of a rigid sphere that we presented recently. It incorporates a calibration stage in which the microphone signals due to a suitable set of calibration sound fields are projected onto the SH decomposition of those same sound fields on the surface of a notional rigid sphere by means of a linear filtering operation. The filter coefficients are computed from the calibration data via a least/squares fit. We present an evaluation of the method based on the application of binaural rendering of the SH decomposition of the signals from an 18/element array that uses a human head as the baffle and that provides 8th ambisonic order. We analyse the accuracy and robustness of our method based on simulated data as well as based on measured data from a prototype.

We present a method for obtaining a spherical harmonic representation of a sound field based on a microphone array along the equator of a rigid spherical scatterer. The two-dimensional plane wave de-composition of the incoming sound field is computed from the microphone signals. The influence of the scatterer is removed under the assumption of distant sound sources, and the result is converted to a spherical harmonic (SH) representation, which in turn can be rendered binaurally. The approach requires an order of magnitude fewer microphones compared to conventional spherical arrays that operate at the same SH order at the expense of not being able to accurately represent non-horizontally-propagating sound fields. Although the scattering removal is not perfect at high frequencies at low harmonic orders, numerical evaluation demonstrates the effectiveness of the approach.

Production of high fidelity spatial audio applications requires individual head-related transfer functions (HRTFs). As the acquisition of HRTF is an elaborate process, interest lies in interpolating full length HRTF from sparse samples. Ear-alignment is a recently developed pre-processing technique, shown to reduce an HRTF’s spherical harmonics order, thus permitting sparse sampling over fewer directions. This paper describes the application of two methods for ear-aligned HRTF interpolation by sparse sampling: Orthogonal Matching Pursuit and Principal Component Analysis. These methods consist of generating unique vector sets for HRTF representation. The methods were tested over an HRTF dataset, indicating that interpolation errors using small sampling schemes may be further reduced by up to 5 dB in comparison with spherical harmonics interpolation.

Reproduction of high quality spatial sound has gained considerable importance with the recent technology developments in the fields of virtual and augmented reality. Recently, the reproduction of binaural signals in the Spherical-Harmonics (SH) domain has been proposed. This is performed by using SH representations of the sound-field and the Head-Related Transfer Function (HRTF). These processes offer the flexibility to control the reproduced binaural signals, by manipulating the sound-field or the HRTFs using algorithms that operate directly in the SH domain. However, in most practical cases, the binaural reproduction is order-limited, which introduces truncation error that has a detrimental effect on the perception of the reproduced signals, mainly due to the truncation of the HRTF. A recent study showed that pre-processing of the HRTF by ear-alignment reduces its effective SH order, which may be beneficial for alleviating the above effect. In this paper, a method to incorporate the pre-processed ear-aligned HRTF into the binaural reproduction process is presented. The method uses Ambisonics representation of the sound-field formulated at the two ears, and is denoted here as Bilateral Ambisonics. The proposed method leads to a significant reduction in errors due to the limited-order reproduction, which yields a substantial improvement in perceived binaural reproduction quality even with SH as low as first order.

Additive noise produced by the recording hardware will contribute to streamed signals from spherical microphone arrays under practical conditions. For the application of binaural reproduction and under the assumption that the noise is uncorrelated between the array channels, the spectral properties and the overall level of the rendered noise in the ear signals have been shown to be strongly influenced by the configuration of the array as well as of the processing pipeline. In a previous investigation, we determined the audibility thresholds for changes in the rendered noise due to listener head rotations as a function of the differences in noise level of individual array channels. In this article, we calibrate the instrumental metric of Composite Loudness Level to the perceptual data and predict audibility of changes in the additive noise due to head rotations for a broad set of array configurations and distributions of the noise levels across array channels. We demonstrate that some types of microphone layouts can produce audible variations even if the noise level is equal in all channels. This is particularly the case for sampling grids that exhibit negative quadrature weights such as the Lebedev and Fliege-Maier grids for some spherical harmonic orders. The analysis of configurations with unevenly distributed noise contributions show that the influence of the noise from individual array channels is determined by the proximity of their virtual location to the relative trajectory of the ears.

Spherical microphone arrays are used to capture spatial sound fields, which can then be rendered via headphones. We use the Real-Time Spherical Array Renderer (ReTiSAR) to analyze and auralize the propagation of sensor self-noise through the processing pipeline. An instrumental evaluation confirms a strong global influence of different array and rendering parameters on the spectral balance and the overall level of the rendered noise. The character of the noise is direction independent in the case of spatially uniformly distributed noise. However, timbre of the rendered self-noise changes with head orientation in the case of spatially non-uniform noise. We determine audibility thresholds of the coloration artifact during head rotations for different array configurations in a perceptual user study.

With the proliferation of high quality virtual reality systems, the demand for high fidelity spatial audio reproduction has grown. This requires individual head-related transfer functions (HRTFs) with high spatial resolution. Acquiring such HRTFs is not always possible, which motivates the need for sparsely sampled HRTFs. Additionally, real-time applications require compact representation of HRTFs. Recently, spherical-harmonics (SH) has been suggested for efficient interpolation and representation of HRTFs. However, representation of sparse HRTFs with a limited SH order may introduce spatial aliasing and truncation errors, which have a detrimental effect on the reproduced spatial audio. This is because the HRTF is inherently of a high spatial order. One approach to overcome this limitation is to pre-process the HRTF, with the aim of reducing its effective SH order. A recent study showed that order-reduction can be achieved by time-alignment of HRTFs, through numerical estimation of the time delays of the HRTFs. In this paper, a new method for pre-processing HRTFs in order to reduce their effective order is presented. The method uses phase-correction based on ear alignment, by exploiting the dual-centering nature of HRTF measurements. In contrast to time-alignment, the phase-correction is performed parametrically, making it more robust to measurement noise. The SH order reduction and ensuing interpolation errors due to sparse sampling were analyzed for these two methods. Results indicate significant reduction in the effective SH order, where only 100 measurements and order 6 are required to achieve a normalized mean square error below -10 dB compared to a fully-sampled, high-order HRTF.

High-fidelity 3D audio experience requires accurate individual head-related transfer function (HRTF) representation. However, the process of measuring individual HRTFs typically involves measurements from hundreds of directions, with specialized and expensive equipment, which makes this process inaccessible for most users. In this paper, a new technique to reconstruct high resolution individual HRTFs from sparse measurements is presented. This is achieved by minimizing the spatial aliasing error in the spherical harmonics (SH) representation of the HRTFs, and by incorporating statistics calculated from a set of reference HRTFs, leading to an optimal minimum mean-square error solution. A quantitative analysis of the proposed method illustrates its benefits even for extreme cases, such as using only 25 individual HRTF measurements and a generic HRTF as a reference.

Spherical microphone arrays facilitate three-dimensional processing and analysis of sound fields in applications such as music recording, beamforming and room acoustics. The frequency bandwidth of operation is constrained by the array configuration. At high frequencies, spatial aliasing leads to side-lobes in the array beam pattern, which limits array performance. Previous studies proposed increasing the number of microphones or changing other characteristics of the array configuration to reduce the effect of aliasing. In this paper we present a method to design beamformers that overcome the effect of spatial aliasing by suppressing the undesired side-lobes through signal processing without physically modifying the configuration of the array. This is achieved by modeling the expected aliasing pattern in a maximum-directivity beamformer design, leading to a higher directivity index at frequencies previously considered to be out of the operating bandwidth, thereby extending the microphone array frequency range of operation. Aliasing cancellation is then extended to other beamformers. A simulation example with a 32-element spherical microphone array illustrates the performance of the proposed method. An experimental example validates the theoretical results in practice.