Huseyin Hacihabiboglu

Modern eXtended Reality (XR) systems are capable of tracking their users' positions within a navigable volume. This opens up the possibility of more realistic interactions with the content not only via head rotations, but also via locomotion. Such capability necessitates the design of audio systems that can generate a perceptually veridical rendering of the sound field at every position which the user can navigate to. While such interactivity can be provided by object-based audio (OBA) renderers such as in 3D computer games and VR applications, the new generation of volumetric visual content requires volumetric capture and rendering of the entire captured scenes, without explicitly identifying/extracting the audio objects therein. This article presents a parametric sound field interpolation method via multi-point sparse plane wave decomposition (SPWD) in the spherical harmonic domain. The proposed approach relies on higher-order Ambisonics (HOA) recordings of sound fields captured at multiple positions. The sound field is separated into direct and diffuse components that are interpolated parametrically and linearly, respectively. The proposed approach is compared with linear interpolation objectively and subjectively to demonstrate its utility.

Differential microphone arrays (DMAs) use multiple omnidirectional microphones for synthesising higher-order microphone directivity patterns. In their most basic form, they can be used to obtain fixed-directivity or horizontally steerable beamformers that can satisfy certain constraints. We propose a vector differential microphone array (VDMA) which is frequency-and direction-invariantly steerable in three dimensions. The proposed design comprises pressure and particle velocity sensors positioned on a circular constellation in a plane and allows extracting the third-order spherical harmonic decomposition of the sound field. This decomposition can then be used to obtain spherically directioninvariant steered beams. Synthesis of a maximum directivity factor (MaxDF) directivity pattern is demonstrated. A closed-form expression for the proposed array's white noise gain (WNG) is derived. The robustness of the proposed design to noise is analysed.

Differential microphone arrays (DMAs) use multiple omnidirectional microphones for synthesising higher-order microphone directivity patterns. In their most basic form, they can be used to obtain fixed-directivity or horizontally steerable beamformers that can satisfy certain constraints. We propose a vector differential microphone array (VDMA) which is frequency- and direction-invariantly steerable in three dimensions. The proposed design comprises pressure and particle velocity sensors positioned on a circular constellation in a plane and allows extracting the third-order spherical harmonic decomposition of the sound field. This decomposition can then be used to obtain spherically direction-invariant steered beams. Synthesis of a maximum directivity factor (MaxDF) directivity pattern is demonstrated. A closed-form expression for the proposed array's white noise gain (WNG) is derived. The robustness of the proposed design to noise is analysed.

Artificial reverberators provide a computationally viable alternative to full-scale room acoustics simulation methods for deployment in interactive, immersive systems. Scattering delay network (SDN) is an artificial reverberator that allows direct parametric control over the geometry of a simulated cuboid enclosure, as well as the directional characteristics of the simulated sound sources and microphones. This paper extends the concept of SDN reverberators to multiple enclosures coupled via an aperture. The extension allows independent control of the acoustical properties of the coupled enclosures and the size of the connecting aperture. Transfer functions of the coupled-volume SDN are derived. The effectiveness of the proposed method is evaluated in terms of rendered energy decay curves in comparison to full-scale ray-tracing models and scale model measurements.

Recording multiple sound sources in a reverberant environment results in convolutive mixtures. Sound sources can be extracted from microphone array recordings of such mixtures using acoustic source separation techniques. Acoustic source separation using recordings obtained from rigid spherical microphone arrays (RSMA) benefit from the representation of sound fields as series of spherical harmonics. More specifically, RSMAs afford increased flexibility in acoustic beamforming and spatial filtering. We propose a data-driven DOA estimation and acoustic source separation method based on a dictionary-based sparse decomposition of sound fields. The proposed method involves identifying the time-frequency bins with contributions from a single source only and those with sensor noise or diffuse sound field components. The former set of bins is used in DOA estimation and beamforming in the sparse decomposition domain. The latter set is used to calculate the diffuse field covariance matrix used in Wiener post-filtering to improve the source separation performance further. We demonstrate the utility of the proposed method via extensive objective and subjective evaluations.

Familiarity with sound sources is known to have a modulatory effect on auditory distance perception. However, the level of familiarity that can affect distance perception is not clearly understood. A subjective experiment that aims to investigate the effects of interpersonal familiarity on auditory distance perception with level-equalized stimuli is reported. The experiment involves a binaural listening task where different source distances between 0.5 and 16 m were emulated by convolving dry speech signals with measured binaural room impulse responses. The experimental paradigm involved level-equalized stimuli comprising speech signals recorded from different-gender couples who have self-reported to have known each other for more than a year with daily interaction. Each subject judged the distances of a total of 15 different speech stimuli from their partner as well as spectrally most similar and most dissimilar strangers, for six different emulated distances. The main finding is that a similar but unfamiliar speaker is localized to be further away than a familiar speaker. Another finding is that the semantic properties of speech can potentially have a modulating effect on auditory distance judgements.

Acoustic features extracted from speech are widely used in problems such as biometric speaker identification and first-person activity detection. However, the use of speech for such purposes raises privacy issues as the content is accessible to the processing party. In this work, we propose a method for speaker and posture classification using intraspeech breathing sounds. Instantaneous magnitude features are extracted using the Hilbert-Huang transform (HHT) and fed into a CNN-GRU network for classification of recordings from the open intraspeech breathing sound dataset, BreathBase, that we collected for this study. Using intraspeech breathing sounds, 87% speaker classification, and 98% posture classification accuracy were obtained.

This paper studies the effects of inter-channel time and level differences in stereophonic reproduction on perceived localization uncertainty, which is defined as how difficult it is for a listener to tell where a sound source is located. Towards this end, a computational model of localization uncertainty is proposed first. The model calculates inter-aural time and level difference cues, and compares them to those associated to free- field point-like sources. The comparison is carried out using a particular distance functional that replicates the increased uncertainty observed experimentally with inconsistent inter-aural time and level difference cues. The model is validated by formal listening tests, achieving a Pearson correlation of 0.99. The model is then used to predict localization uncertainty for stereophonic setups and a listener in central and off-central positions. Results show that amplitude methods achieve a slightly lower localization uncertainty for a listener positioned exactly in the center of the sweet spot. As soon as the listener moves away from that position, the situation reverses, with time-amplitude methods achieving a lower localization uncertainty.

Estimation of the direction-of-arrival (DOA) of sound sources is an important step in sound field analysis. Rigid spherical microphone arrays allow the calculation of a compact spherical harmonic representation of the sound field. The standard method for analyzing sound fields recorded using such arrays is steered response power (SRP) maps wherein the source DOA can be estimated as the steering direction that maximizes the output power of a maximally directive beam. This approach is computationally costly since it requires steering the beam in all possible directions. This paper presents an extension to SRP called steered response power density (SRPD) and an associated, signal-adaptive search method called hierarchical grid refinement for reducing the number of steering directions needed for DOA estimation. The proposed method can localize near-coherent as well as incoherent sources while jointly providing the number of prominent sources in the scene. It is shown to be robust to reverberation and additive white noise. An evaluation of the proposed method using simulations and real recordings under highly reverberant conditions as well as a comparison with the state-of-the-art methods are presented.