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Large-scale Room Impulse Response (RIR) measurements are required to accurately determine a room's acoustic response to different source-listener configurations. RIR reconstruction methods are often used to reduce these measurement costs. Prior knowledge of room acoustic parameters can ensure reliable and robust RIR reconstruction. This paper proposes a method to reconstruct RIRs based on reflection source locations and time-frequency-direction-dependent reflection magnitude response estimated from a single spherical microphone array measurement.

This paper presents a dataset of spatial room impulse responses (SRIRs) and 360° stereoscopic video captures of a variable acoustics laboratory. A total of 34 source positions are measured with 8 different acoustic panel configurations, resulting in a total of 272 SRIRs. The source positions are arranged in 30° increments at concentric circles of radius 1.5, 2, and 3 m measured with a directional studio monitor, as well as 4 extra positions at the room corners measured with an omnidirectional source.

Despite there being clear evidence for attentional effects in biological spatial hearing, relatively few machine hearing systems exploit attention in binaural sound localisation. This paper addresses this issue by proposing a novel binaural machine hearing system with temporal attention for robust localisation of sound sources in noisy and reverberant conditions. A convolutional neural network is employed to extract noise-robust localisation features, which are similar to interaural phase difference, directly from phase spectra of the left and right ears for each frame.

Given a sound field generated by a sparse distribution of impulse image sources, can the continuous 3D positions and amplitudes of these sources be recovered from discrete, band-limited measurements of the field at a finite set of locations, e.g. , a multichannel room impulse response? Borrowing from recent advances in super-resolution imaging, it is shown that this non-linear, non-convex inverse problem can be efficiently relaxed into a convex linear inverse problem over the space of Radon measures in R^3 .

Low frequency personal sound zones can be created by controlling the sound pressure in separate spatially confined regions. The performance of a sound zone system using wireless communication may be degraded due to potential packet losses. In this paper, we propose robust FIR filters for low-frequency sound zone system by incorporating information about the expected packet losses into the design.

An interpolation method for region-to-region acoustic transfer functions (ATFs) based on kernel ridge regression with an adaptive kernel is proposed. Most current ATF interpolation methods do not incorporate the acoustic properties for which measurements are performed. Our proposed method is based on a separate adaptation of directional weighting functions to directed and residual reverberations, which are used for adapting kernel functions. Thus, the proposed method can not only impose constraints on fundamental acoustic properties, but can also adapt to the acoustic environment.

We propose a novel approach for blind room impulse response (RIR) estimation systems in the context of a downstream application scenario, far-field automatic speech recognition (ASR). We first draw the connection between improved RIR estimation and improved ASR performance, as a means of evaluating neural RIR estimators.

A method of interpolating the acoustic transfer function (ATF) between regions that takes into account both the physical properties of the ATF and the directionality of region configurations is proposed. Most spatial ATF interpolation methods are limited to estimation in the region of receivers. A kernel method for region-to-region ATF interpolation makes it possible to estimate the ATFs for both source and receiver regions from a discrete set of ATF measurements.

A model of a room impulse response (RIR) is useful for a wide range of applications. Typically, the early part of an RIR is sparse, and its sparse structure allows for accurate and simple modeling of the RIR. The existing L-p (0 < p ≤ 1)-norm-based methods suffer from the sensitivity to the user-selected regularization parameters or a high computational burden. In this work, we propose to reconstruct the sparse model for the early part of RIRs with sparse Bayesian learning (SBL).