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With the strong growth of assistive and personal listening devices, natural sound rendering over headphones is becoming a necessity for prolonged listening in multimedia and virtual reality applications. The aim of natural sound rendering is to naturally recreate the sound scenes with the spatial and timbral quality as natural as possible, so as to achieve a truly immersive listening experience. However, rendering natural sound over headphones encounters many challenges. This tutorial article presents signal processing techniques to tackle these challenges to assist human listening.

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In this paper, we present a novel framework of beamforming robust for a microphone array rotation. In most array signal processing methods, the time-invariant transfer system from a source to a microphone is assumed for calculating a spatial filter. This assumption makes it difficult to use the microphone array in real situations since sources and the microphone array may move. In this work, we focus on one such movement, the array’s rotation. The key in our method is to use a regularly circular microphone array where microphones are equally spaced on a circle’s circumference.

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Head-Related Transfer Function (HRTF) individualization is critical for immersive and realistic spatial audio rendering in augmented/virtual reality. Neither measurements nor simulations using 3D scans of head/ear are scalable for practical applications. More efficient machine learning approaches are being explored recently, to predict HRTFs from ear images or anthropometric features. However, it is not yet clear whether such models can provide an alternative for direct measurements or high-fidelity simulations. Here, we aim to address this question.

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We have been exploring the integration of sparse recovery methods into the ray space transform over the past years and now demonstrate the potential and benefits of beamforming and upscaling signals in the integrated ray space and sparse recovery domain. A primary advantage of the ray space approach derives from its robust ability to integrate information from multiple arrays and viewpoints. Nonetheless, for a given viewpoint, the ray space technique requires a dense array that can be divided into sub-arrays enabling the plenacoustic approach to signal processing.

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Noise reduction in B-format recordings is particularly challenging as it concurrently requires to suppress undesired signals and preserve the spatial properties of the acoustic environment. In particular, wind noise poses an undesirable acoustic condition outdoors. In this work, methods to reduce wind noise while limiting the spatial distortions of the original signal are proposed based on recent works of the present authors.

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Noise reduction in B-format recordings is particularly challenging as it concurrently requires to suppress undesired signals and preserve the spatial properties of the acoustic environment. In particular, wind noise poses an undesirable acoustic condition outdoors. In this work, methods to reduce wind noise while limiting the spatial distortions of the original signal are proposed based on recent works of the present authors.

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This paper presents a novel 3DoF+ system that allows to navigate, i.e., change position, in scene-based spatial audio content beyond the sweet spot of a Higher Order Ambisonics recording. It is one of the first such systems based on sound capturing at a single spatial position. The system uses a parametric decomposition of the recorded sound field. For the synthesis, only coarse distance information about the sources is needed as side information but not the exact number of them.

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A sensor (microphone) placement method based on mutual information for spatial sound field recording is proposed. The sound field recording methods using distributed sensors enable the estimation of the sound field inside a target region of arbitrary shape; however, it is a difficult task to find the best placement of sensors. We focus on the mutual-information-based sensor placement method in which spatial phenomena are modeled as a Gaussian process (GP).

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Active noise control (ANC) over space is a well-researched topic where multi-microphone, multi-loudspeaker systems are designed to minimize the noise over a spatial region of interest. In this paper, we perform an initial study on the more complex problem of simultaneous noise control over multiple target regions using a single ANC system. In particular, we investigate the maximum active noise control performance over the multiple target regions, given a particular setup of secondary loudspeakers.

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