The localization of sounding objects has been extensively studied. In certain situations, however, an object of interest may not emit sound while another sound source is active. For instance, this occurs when a silent human listens to music and needs to be localized, e.g., to optimize the playback for the listener’s position. In this study, we present a method for localizing silent listeners: By continuously estimating room impulse responses using a loudspeaker emitting a known signal and a microphone array, we can detect subtle variations in the room impulse response over time, which are induced by the listener’s motion. Our results, evaluated in two different rooms, indicate that six room impulse responses, estimated using two successive 20 Hz-24 kHz sine sweeps of 1 s duration and each captured at three spatial points, are sufficient to localize a sitting listener within 35 cm accuracy 82 % and a standing listener 93 % of the time.

A study is presented in which a contrastive learning approach is used to extract low-dimensional representations of the acoustic environment from single-channel, reverberant speech signals. Convolution of room impulse responses (RIRs) with anechoic source signals is leveraged as a data augmentation technique that offers considerable flexibility in the design of the upstream task. We evaluate the embeddings across three different downstream tasks, which include the regression of acoustic parameters reverberation time RT60and clarity index C50, and the classification into small and large rooms. We demonstrate that the learned representations generalize well to unseen data and perform similarly to a fully-supervised baseline.

The estimation of reverberation time from real-world signals plays a central role in a wide range of applications. In many scenarios, acoustic conditions change over time which in turn requires the estimate to be updated continuously. Previously proposed methods involving deep neural networks were mostly designed and tested under the assumption of static acoustic conditions. In this work, we show that these approaches can perform poorly in dynamically evolving acoustic environments. Motivated by a recent trend towards data-centric approaches in machine learning, we propose a novel way of generating training data and demonstrate, using an existing deep neural network architecture, the considerable improvement in the ability to follow temporal changes in reverberation time.

Sound reproduction systems may highly benefit from detailed knowledge of the acoustic space to enhance the spatial sound experience. This article presents a room geometry inference method based on identification of reflective boundaries using a high-resolution direction-of-arrival map produced via room impulse responses (RIRs) measured with a linear loudspeaker array and a single microphone. Exploiting the sparse nature of the early part of the RIRs, Elastic Net regularization is applied to obtain a 2D polar-coordinate map, on which the direct path and early reflections appear as distinct peaks, described by their propagation distance and direction of arrival. Assuming a separable room geometry with four side-walls perpendicular to the floor and ceiling, and imposing pre-defined geometrical constraints on the walls, the 2D-map is segmented into six regions, each corresponding to a particular wall. The salient peaks within each region are selected as candidates for the first-order wall reflections, and a set of potential room geometries is formed by considering all possible combinations of the associated peaks. The room geometry is then inferred using a cost function evaluated on the higher-order reflections computed via beam tracing. The proposed method is tested with both simulated and measured data.

A weighted least squares beam shaping technique for sound field control using a loudspeaker array is proposed. Given a desired spatial response at prescribed control points, the space-time filter is designed by solving a least squares minimization problem. To reduce the computational effort, we propose to place control points only along an arc of circumference centered at the center of the array and passing through a region of interest. Furthermore, we adopt a weighted least squares approach for the design of the space-time filter, so that control points at directions towards which we admit a looser control of the sound field are less relevant in the filter design. The choice of the weights depends on the specific application and we demonstrate the feasibility of the proposed approach for sound zones scenario with one bright and one dark zone.

Room geometry inference is concerned with the localization of reflective boundaries in an enclosed space. This paper outlines a method for inferring room geometry based on the positions of loudspeakers and real or image microphones, which are computed using sets of times of arrival (TOAs) obtained from room impulse responses (RIRs). These RIRs describe the acoustic propagation between the loudspeakers in an array and a single microphone. First, peaks corresponding to TOAs in these RIRs are detected and labeled using an automated method. Second, the labeled TOA sets are used to estimate the real and image microphone positions, with knowledge of the loudspeaker array geometry. Third, using all these positions, the positions of reflection points on the available reflectors in the room are determined. The reflection points determine the reflectors' locations and orientations. This approach is largely automated and usable in real-world scenarios.

Echo labeling, the challenging task of assigning acoustic reflections to image sources, is equivalent to the highly-important disambiguation task in room geometry inference. A method using the Radon transform, an image processing tool, is proposed to address this challenge. The method relies on acoustic wavefront detection in room impulse response stacks, obtained with a uniform linear array of loudspeakers and one microphone. We show in our experiments that the proposed method can both label and detect echoes.