Scene-based spatial audio formats, such as Ambisonics, are playback system agnostic and may therefore be favoured for delivering immersive audio experiences to a wide range of (potentially unknown) devices. The number of channels required to deliver high spatial resolution Ambisonic audio, however, can be prohibitive for low-bandwidth applications. Therefore, this paper proposes a compression codec, which is based upon the parametric higher-order Directional Audio Coding (HO-DirAC) model. The encoder downmixes the higher-order Ambisonic (HOA) input audio into a reduced number of signals, which are accompanied by perceptually-motivated scene parameters. The downmixed audio is coded using a perceptual audio coder, whereas the parameters are grouped into perceptual bands, quantized, and downsampled. On the decoder side, low Ambisonic orders are fully recovered. Not fully recoverable HOA components are synthesized according to the parameters. The results of a listening test indicate that the proposed parametric spatial audio codec can improve the adopted perceptual audio coder, especially at low to medium-high bitrates, when applied to fifth-order HOA signals.

Delivering high-quality spatial audio in the Ambisonics format requires extensive data bandwidth, which may render it inaccessible for many low-bandwidth applications. Existing widely-available multi-channel audio compression codecs are not designed to consider the characteristic inter-channel relations inherent to the Ambisonics format, and thus may not leverage this knowledge to optimise the compression. Therefore, this article proposes a spatial audio compression algorithm, based on a novel reformulation of the Higher-Order Directional Audio Coding (HO-DirAC) method, which is specifically intended for compressing higher-order Ambisonic audio streams. The methodology builds upon the concept of a spherical filter bank acting in the spherical harmonic domain. This results in directionally constrained sound-field estimates and parameterization, which may be utilized to reconstruct the input Ambisonic signals with minimal perceived loss of quality. The results of a listening experiment indicate high perceptual quality when using six or more audio transport channels to deliver fifth-order (36 channels) Ambisonic sound scenes. The proposed formulation is also designed with low computational complexity in mind and may therefore be well suited for compressing Ambisonic sound scenes for a wide range of applications.

This article proposes a parametric signal-dependent method for the task of encoding microphone array signals into Ambisonic signals. The proposed method is presented and evaluated in the context of encoding a simulated seven-sensor microphone array, which is mounted on an augmented reality headset device. Given the inherent flexibility of the Ambisonics format, and its popularity within the context of such devices, this array configuration represents a potential future use case for Ambisonic recording. However, due to its irregular geometry and non-uniform sensor placement, conventional signal-independent Ambisonic encoding is particularly limited. The primary aims of the proposed method are to obtain Ambisonic signals over a wider frequency band-width, and at a higher spatial resolution, than would otherwise be possible through conventional signal-independent encoding. The proposed method is based on a multi-source sound-field model and employs spatial filtering to divide the captured sound-field into its individual source and directional ambient components, which are subsequently encoded into the Ambisonics format at an arbitrary order. It is demonstrated through both objective and perceptual evaluations that the proposed parametric method outperforms conventional signal-independent encoding in the majority of cases.

Decomposing a sound-field into its individual components and respective parameters can represent a convenient first-step towards offering the user an intuitive means of controlling spatial audio effects and sound-field modification tools. The majority of such tools available today, however, are instead limited to linear combinations of signals or employ a basic single-source parametric model. Therefore, the purpose of this paper is to present a parametric framework, which seeks to overcome these limitations by first dividing the sound-field into its multi-source and ambient components based on estimated spatial parameters. It is then demonstrated that by manipulating the spatial parameters prior to reproducing the scene, a number of sound-field modification and spatial audio effects may be realised; including: directional warping, listener translation, sound source tracking, spatial editing workflows and spatial side-chaining. Many of the effects described have also been implemented as real-time audio plug-ins, in order to demonstrate how a user may interact with such tools in practice.

A method for computing and sharpening angular spectra, derived from low-order ambisonic signals, is presented in this paper, which is intended for high-resolution directional sound-field visualisation. The method relies on a re-assignment principle, whereby the directional energy for each grid point is assigned to a new direction, which corresponds to a direction-of-arrival (DoA) estimate within a spatially-localised region, centred around the respective grid point. This leads to the concentration of energy around the true sources, and hence, to sharper angular spectra than that of steered response power (SRP) beamformers of maximum directivity, with the same order of ambisonic input. It is demonstrated that the proposed method, when using low-order input, can achieve similar results to the SRP approach of much higher order.