A Head-Mounted Microphone Array for Binaural Rendering

Ahrens, Jens; Helmholz, Hannes; Alon, David Lou; Garí, Sebastià V. Amengual

doi:10.1109/i3da48870.2021.9610892

Cited by 9 publications

(4 citation statements)

References 13 publications

(16 reference statements)

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“…The microphone array selected for this study comprised seven DPA 4061-OC-C-B00 omni-directional microphones, which were distributed approximately uniformly around the circumference of a pair of eyeglasses worn by a KEMAR head-and-torso simulator. Such a configuration has previously been used, for example, in [35], and is depicted in Fig. 2.…”

Section: Test Apparatusmentioning

confidence: 99%

“…For example, a recent study involving a seven-sensor head-worn microphone array demonstrated poor encoding performance above 1.5 kHz, when using a conventional signal-independent encoding approach [34]. This has therefore motivated recent alternative encoding proposals, such as seeking to exploit the properties of an equatorial arrangement of sensors [35], or employing parametric/signal-dependent processing [36,34], in order to help alleviate the encoding limitations of such arrays. However, it may be argued that circumventing any conversion into the intermediate Ambisonics format, and instead mapping the input array signals directly to the binaural channels, should represent the more optimal rendering strategy.…”

Section: Introductionmentioning

confidence: 99%

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Six-Degrees-of-Freedom Binaural Reproduction of Head-Worn Microphone Array Capture

Mccormack,

Meyer-Kahlen,

Lou Alon

et al. 2023

J. Audio Eng. Soc.

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This article formulates and evaluates four different methods for six-degrees-of-freedom binaural reproduction of head-worn microphone array recordings, which may find application within future augmented reality contexts. Three of the explored methods are signalindependent, utilizing least-squares, magnitude least-squares, or plane wave decompositionbased solutions. Rotations and translations are realized by applying directional transformations to the employed spherical rendering or optimization grid. The fourth considered approach is a parametric signal-dependent alternative, which decomposes the array signals into directional and ambient components using beamformers. The directional components are then spatialized by applying binaural filters corresponding to the transformed directions, whereas the ambient sounds are reproduced using the magnitude least-squares solution. Formal perceptual studies were conducted, whereby test participants rated the perceived relative quality of the four binaural rendering methods being evaluated. Of the three signal-independent approaches, the magnitude least-squares solution was rated the highest. The parametric approach was then rated higher than the magnitude least-square solution when the listeners were permitted to move away from the recording point.

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Section: Test Apparatusmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Six-Degrees-of-Freedom Binaural Reproduction of Head-Worn Microphone Array Capture

Mccormack,

Meyer-Kahlen,

Lou Alon

et al. 2023

J. Audio Eng. Soc.

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show abstract

“…Ambisonics rotation is easily performed by applying a time-and frequency-independent rotation matrix to the Ambisonics coefficients [181][182][183][184][185][186]. The challenges of headrotation-enabled binaural reproduction for recording by non-standard or wearable microphone arrays [187][188][189] are still the subject of ongoing research.…”

Section: Rotation and Translationmentioning

confidence: 99%

Spatial audio signal processing for binaural reproduction of recorded acoustic scenes – review and challenges

Rafaely

Tourbabin²,

Habets³

et al. 2022

Acta Acust.

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Spatial audio has been studied for several decades, but has seen much renewed interest recently due to advances in both software and hardware for capture and playback, and the emergence of applications such as virtual reality and augmented reality. This renewed interest has led to the investment of increasing efforts in developing signal processing algorithms for spatial audio, both for capture and for playback. In particular, due to the popularity of headphones and earphones, many spatial audio signal processing methods have dealt with binaural reproduction based on headphone listening. Among these new developments, processing spatial audio signals recorded in real environments using microphone arrays plays an important role. Following this emerging activity, this paper aims to provide a scientific review of recent developments and an outlook for future challenges. This review also proposes a generalized framework for describing spatial audio signal processing for the binaural reproduction of recorded sound. This framework helps to understand the collective progress of the research community, and to identify gaps for future research. It is composed of five main blocks, namely: the acoustic scene, recording, processing, reproduction, and perception and evaluation. First, each block is briefly presented, and then, a comprehensive review of the processing block is provided. This includes topics from simple binaural recording to Ambisonics and perceptually motivated approaches, which focus on careful array configuration and design. Beamforming and parametric-based processing afford more flexible designs and shift the focus to processing and modeling of the sound field. Then, emerging machine- and deep-learning approaches, which take a further step towards flexibility in design, are described. Finally, specific methods for signal transformations such as rotation, translation and enhancement, enabling additional flexibility in reproduction and improvement in the quality of the binaural signal, are presented. The review concludes by highlighting directions for future research.

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“…By contrast, applications that demand for transfer-plausible rendering will most likely comprise of compact arrays, for example worn on the head of a user [9]. Well-designed compact arrays allow for applying a beamformer directed to a source in the room to obtain a reference signal.…”

Section: Introductionmentioning

confidence: 99%

Blind Directional Room Impulse Response Parameterization from Relative Transfer Functions

Meyer-Kahlen

Schlecht

2022

2022 International Workshop on Acoustic Signal Enhancement (IWAENC)

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Acquiring information about an acoustic environment without conducting dedicated measurements is an important problem of forthcoming augmented reality applications, in which real and virtual sound sources are combined. We propose a straightforward method for estimating directional room impulse responses from running signals. We adaptively identify relative transfer functions between the output of a beamformer pointing into the direction of a single active sound source and the complete set of spherical harmonics domain signals, representing all directions. To this end, estimation is performed with a frequency domain recursive least squares algorithm. Then, parameters such as the directions of arrival of early reflections and the reverberation time are extracted. Estimation of the direct-to-reverberant ratio requires dedicated processing. We show examples of successful estimation from speech signals, based on a simulated and a measured response.

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A Head-Mounted Microphone Array for Binaural Rendering

Cited by 9 publications

References 13 publications

Six-Degrees-of-Freedom Binaural Reproduction of Head-Worn Microphone Array Capture

Six-Degrees-of-Freedom Binaural Reproduction of Head-Worn Microphone Array Capture

Spatial audio signal processing for binaural reproduction of recorded acoustic scenes – review and challenges

Blind Directional Room Impulse Response Parameterization from Relative Transfer Functions

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