This paper describes features in the ear shape that change across a population of ears and explores the corresponding changes in ear acoustics. The statistical analysis conducted over the space of ear shapes uses a kernel principal component analysis (KPCA). Further, it utilizes the framework of large deformation diffeomorphic metric mapping and the vector space that is constructed over the space of initial momentums, which describes the diffeomorphic transformations from the reference template ear shape. The population of ear shapes examined by the KPCA are 124 left and right ear shapes from the SYMARE database that were rigidly aligned to the template (population average) ear. In the work presented here we show the morphological variations captured by the first two kernel principal components, and also show the acoustic transfer functions of the ears which are computed using fast multipole boundary element method simulations.

This paper describes the generation of a morphable model for external ear shapes. The aim for the morphable model is to characterize an ear shape using only a few parameters in order to assist the study of morphoacoustics. The model is derived from a statistical analysis of a population of 58 ears from the SYMARE database. It is based upon the framework of large deformation diffeomorphic metric mapping (LDDMM) and the vector space that is constructed over the space of initial momentums describing the diffeomorphic transformations. To develop a morphable model using the LDDMM framework, the initial momentums are analyzed using a kernel based principal component analysis. In this paper, we examine the ability of our morphable model to construct test ear shapes not included in the principal component analysis.

Characterizing sound field diffuseness has many practical applications, from room acoustics analysis to speech enhancement and sound field reproduction. In this paper, we investigate how spherical microphone arrays (SMAs) can be used to characterize diffuseness. Due to their specific geometry, SMAs are particularly well suited for analyzing the spatial properties of sound fields. In particular, the signals recorded by an SMA can be analyzed in the spherical harmonic (SH) domain, which has special and desirable mathematical properties when it comes to analyzing diffuse sound fields. We present a new measure of diffuseness, the COMEDIE diffuseness estimate, which is based on the analysis of the SH signal covariance matrix. This algorithm is suited for the estimation of diffuseness arising either from the presence of multiple sources distributed around the SMA or from the presence of a diffuse noise background. As well, we introduce the concept of a diffuseness profile, which consists in measuring the diffuseness for several SH orders simultaneously. Experimental results indicate that diffuseness profiles better describe the properties of the sound field than a single diffuseness measurement.

Constructing accurate models that represent the underlying structure of Big Data is a costly process that usually constitutes a compromise between computation time and model accuracy. Methods addressing these issues often employ parallelisation to handle processing. Many of these methods target the Support Vector Machine (SVM) and provide a significant speed up over batch approaches. However, the convergence of these methods often rely on multiple passes through the data. In this paper, we present a parallelised algorithm that constructs a model equivalent to a serial approach, whilst requiring only a single pass of the data. We first employ the Kernel Recursive Least Squares (KRLS) algorithm to construct several models from subsets of the overall data. We then show that these models can be combined using KRLS to create a single compact model. Our parallelised KRLS methodology significantly improves execution time and demonstrates comparable accuracy when compared to the parallel and serial SVM approaches.

In this paper, we propose a new strategy to obtain superresolution maps of the sound field recorded by a spherical microphone array. In recent works, we have demonstrated that sparse recovery (SR) algorithms based on the minimisation of the lpnorm with 0pnorm when p<;1 is that it is a non-convex optimisation problem, thus it is likely that the algorithm converges to a local minimum. In this paper we show that we can improve the convergence of our SR acoustic imaging methods by providing, to the SR solver, priming information relating to the spatial location of the sound sources. This information can be acquired with a pre-processing, coarse analysis using standard blind source separation or direction-of-arrival techniques. Simulation results indicate that this approach can provide accurate estimates of the positions of multiple, simultaneous sound sources in the presence of noise or reverberation and even in an under-determined situation.

This paper describes how Large Deformation Diffeomorphic Metric Mapping (LDDMM) can be coupled with a Fast Multipole (FM) Boundary Element Method (BEM) to investigate the relationship between morphological changes in the head, torso, and outer ears and their acoustic filtering (described by Head Related Transfer Functions, HRTFs). The LDDMM technique provides the ability to study and implement morphological changes in ear, head and torso shapes. The FM-BEM technique provides numerical simulations of the acoustic properties of an individual's head, torso, and outer ears. This paper describes the first application of LDDMM to the study of the relationship between a listener's morphology and a listener's HRTFs. To demonstrate some of the new capabilities provided by the coupling of these powerful tools, we morph the shape of a listener's ear, while keeping the torso and head shape essentially constant, and show changes in the acoustics. We validate the methodological framework by mapping the complete morphology of one listener to a target listener and obtaining the target listener's HRTFs. This work utilizes the data provided by the Sydney York Morphological and Acoustic Recordings of Ears (SYMARE) database.

Spherical Microphone Arrays (SMAs) constitute a powerful tool for analyzing the spatial properties of sound fields. However, the performance of SMA-based signal processing algorithms ultimately depends on the physical characteristics of the array. In particular, the range of frequencies over which an SMA provide rich spatial information is conditioned by the size of the array, the angular position of the sensors and other factors. In this work, we investigate the design of SMAs offering a wider frequency range of operation than that offered by conventional designs. To achieve this goal, microphones are distributed both on and at a distance from the surface of a rigid spherical baffle. The contributions of the paper are as follows. First, we present a general framework for modeling SMAs whose sensors are located at different distances from the array center and calculating optimal filters for the decomposition of the sound field into spherical harmonic modes. Second, we present an optimization method to design multi-radius SMAs with an optimally wide frequency range of operation given the total number of sensors available and target spatial resolution. Lastly, based on the optimization results, we built a prototype dual-radius SMA with 64 microphones. We present measurement results for the prototype microphone array and compare these results with theory.

This paper introduces the process for creating the Sydney York Morphological and Acoustic Recordings of Ears (SYMARE) database. The SYMARE database supports research exploring the relationship between the morphology of human outer ears and their acoustic filtering properties-a relationship that is viewed by many as holding the key to human spatial hearing and the future of 3D personal audio. The SYMARE database is comprised of acoustically measured head-related impulse responses for 61 listeners (48 male/13 female), multiple high-resolution surface mesh models (upper torso, head and ears) for these listeners obtained from magnetic resonance imaging (MRI) data, and the corresponding simulated HRIR data for these listeners generated using the Fast Multipole Boundary Element Method (FM-BEM). In this work, we compare acoustically measured HRIR data for 61 listeners with the listeners' corresponding simulated HRIR data generated using the FM-BEM.

Spherical microphone arrays are a powerful tool for sound field analysis. In previous work, we have shown that sparse recovery can be used to arbitrarily increase the resolution of the sound field recorded by a spherical microphone array. Because these super-resolution techniques rely on the assumption that the sound field results from a few dominant plane waves, they are not robust to the presence of noise or reverberation. In this paper we propose a simple method to separate the sound field into a directional component and a diffuse component prior to applying sparse recovery techniques. Simulations show that this pre-processing could dramatically improve the results of sparse recovery in noisy or reverberant environments.

Spherical microphone arrays provide a powerful tool for examining source localization and direction of arrival (DOA) estimation in the spherical harmonic domain. In previous work, we have investigated applying instantaneous independent component analysis (ICA) or sparse recovery separately in the spherical harmonic domain for DOA estimation. These algorithms work reasonably well, but rely on different signal characteristics: namely statistical independence or the spatial distribution of sources. In this paper, we describe methods to combine the ICA and sparse recovery algorithms to improve DOA estimation. The simulation results indicate that combining ICA and sparse recovery leads to more robust DOA estimation.

In this paper, we present a novel beamforming algorithm that is designed for spherical microphone arrays and formulated in the spherical harmonic domain. The proposed algorithm employs sparse recovery, a compressed sensing technique, and assumes the position of the source signals are unknown. A formal listening test was conducted to evaluate the performance of the proposed algorithm and the results indicate the effectiveness of the proposed algorithm.

In this paper, a novel algorithm for upscaling ambisonic sound scenes in the frequency domain is presented. This algorithm makes use of compressed sensing techniques to calculate a set of upscaling filters. These filters are then used to increase the spherical harmonic order of a set of ambisonic sound signals to higher orders. Upscaled ambisonic sound scenes have a greater spatial resolution, which allows more loudspeakers to be used during the playback, resulting in a larger sweet spot and improved sound quality. A formal listening test was conducted to evaluate the perceptual quality of sound fields reproduced using this technique. Results show that the proposed algorithm significantly improves the perceptual fidelity of the sound field reproduction, in comparison to classical ambisonic methods.

In this paper, we present a novel multichannel dereverberation algorithm that enhances a target signal in a reverberant environment. The proposed algorithm is designed for a spherical microphone array and formulated in the spherical harmonic domain. The algorithm employs sparse recovery, a compressed sensing technique, to estimate the position of the target signal and its early reflections. Room impulse responses are obtained according to the estimations and the MINT (the multiple-input/output inverse-filtering theorem) is used to calculate the inverse filters. The performance of the proposed method is evaluated using computer simulation and our results indicate the effectiveness of the proposed dereverberation algorithm.

This paper introduces the process for creating the Sydney York Morphological and Acoustic Recordings of Ears (SYMARE) database. The SYMARE database supports research exploring the relationship between the morphology of human outer ears and their acoustic filtering properties - a relationship that is viewed by many as holding the key to human spatial hearing and the future of 3D personal audio. The SYMARE database is comprised of acoustically measured head-related impulse responses for 60 listeners, multiple high-resolution surface mesh models (upper torso, head and ears) for these listeners obtained from magnetic resonance imaging (MRI) data, and the corresponding simulated HRIR data for these listeners generated using the Fast Multipole Boundary Element Method (FM-BEM). In this work, we compare acoustically measured HRIR data for ten listeners with the listeners' corresponding simulated HRIR data generated using the FM-BEM.

A novel technique for time domain spatial sound reproduction using compressed sensing is presented. The presented technique is based on the application of compressed sensing theory, which is used to improve the accuracy of the reconstructed sound field. In addition, singular value decomposition is also applied, which acts to significantly reduce the size of the data set to process, thus making it efficient and realisable for real-time applications. Results are presented from the preliminary performance evaluation of the compressed sensing technique in comparison to the Higher Order Ambisonic reconstruction technique.