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Jens Ahrens

Publications and source records attributed to Jens Ahrens.

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Introduction to Ambisonics, Part 1: The Part With No Math

The present document is Part 1 of a 2-part introduction to ambisonics and aims at readers who would like to work practically with ambisonics. We leave out deep technical details in this part and focus on helping the reader to develop an intuitive understanding of the underlying concept. We explain what ambisonic signals are, how they can be obtained, what manipulations can be applied to them, and how they can be reproduced to a listener. We provide a variety of audio examples that illustrate the matter. Part 2 of this introduction into ambisonics is provided in a separate document and aims at readers who would like to understand the mathematical details.

eess.AS

Perceptually Transparent Binaural Auralization of Simulated Sound Fields

Contrary to geometric acoustics-based simulations where the spatial information is available in a tangible form, it is not straightforward to auralize wave-based simulations. A variety of methods have been proposed that compute the ear signals of a virtual listener with known head-related transfer functions from sampling either the sound pressure or the particle velocity (or both) of the simulated sound field. This article summarizes the most common binaural auralization methods with and without intermediate ambisonic representation of volumetrically sampled sound pressure or sound pressure and particle velocity sampled on spherical or cubical surfaces and presents a perceptual validation thereof. A triangular test ($N=19$) confirmed that all evaluated grids resulted in a perceptually transparent auralization for the three tested sound incidence angles under reverberant conditions. Under anechoic conditions, only the high-density spherical and cubical surface grids lead to transparent auralization. All tested methods are available open source in the Chalmers Auralization Toolbox that accompanies this article.

eess.AS

Direct and Residual Subspace Decomposition of Spatial Room Impulse Responses

Psychoacoustic experiments have shown that directional properties of the direct sound, salient reflections, and the late reverberation of an acoustic room response can have a distinct influence on the auditory perception of a given room. Spatial room impulse responses (SRIRs) capture those properties and thus are used for direction-dependent room acoustic analysis and virtual acoustic rendering. This work proposes a subspace method that decomposes SRIRs into a direct part, which comprises the direct sound and the salient reflections, and a residual, to facilitate enhanced analysis and rendering methods by providing individual access to these components. The proposed method is based on the generalized singular value decomposition and interprets the residual as noise that is to be separated from the other components of the reverberation. Large generalized singular values are attributed to the direct part, which is then obtained as a low-rank approximation of the SRIR. By advancing from the end of the SRIR toward the beginning while iteratively updating the residual estimate, the method adapts to spatio-temporal variations of the residual. The method is evaluated using a spatio-spectral error measure and simulated SRIRs of different rooms, microphone arrays, and ratios of direct sound to residual energy. The proposed method creates lower errors than existing approaches in all tested scenarios, including a scenario with two simultaneous reflections. A case study with measured SRIRs shows the applicability of the method under real-world acoustic conditions. A reference implementation is provided.

eess.AS

Ambisonic Encoding of Signals From Spherical Microphone Arrays

This document illustrates how to process the signals from the microphones of a rigid-sphere higher-order ambisonic microphone array so that they are encoded with N3D normalization and ACN channel order and thereby can be used with the standard ambisonic software tools such as SPARTA and the IEM Plugin Suite. A MATLAB script is provided.

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Ambisonic Encoding of Signals From Equatorial Microphone Arrays

The equatorial microphone array presented in (Ahrens et al., 2021) computes a spherical harmonic (SH) representation of a sound field based on pressure sensors along the equator of a rigid spherical baffle. The original formulation uses complex-valued SH basis functions. This is inconvenient if the SH representation of the captured sound field is intended to be stored in time domain by means of real-valued audio signals as it is common in the spatial audio format of ambisonics. The present document summarizes the modifications that need to be applied to the mathematical formulation from (Ahrens et al., 2021) to produce an ambisonic representation of the captured sound field that is compatible with the established ambisonic software tools like SPARTA and the IEM Plugin Suite. An example MATLAB script that implements this formulation is provided.

eess.AS

Binaural Audio Rendering in the Spherical Harmonic Domain: A Summary of the Mathematics and its Pitfalls

The present document reviews the mathematics behind binaural rendering of sound fields that are available as spherical harmonic expansion coefficients. This process is also known as binaural ambisonic decoding. We highlight that the details entail some amount peculiarity so that one has to be well aware of the precise definitions that are chosen for some of the involved quantities to obtain a consistent formulation. We also discuss what sets of definitions produce ambisonic signals that are compatible with the most common software tools that are available.

cs.SD