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.

When distances between microphone pairs are larger than the half-wavelength of signals, source localization methods using cross-correlation such as time-difference-of-arrival (TDOA), steered response power (SRP) are commonly used in practice. We present here a novel model that expresses microphone pairwise cross-correlations as a sum of autocorrelations of source signals shifted by the relative delays of the signals arriving at the microphone pairs, and weighted by the source power and the distances between the sources and the microphone pairs. The model is formulated as a linear inverse problem and is sparse with respect to the source power map. The source power map, which directly shows the locations of all the sound sources, can be reconstructed using ℓ1-norm minimization algorithms. We demonstrate the effectiveness of our model in a wildlife monitoring application, where the goal is to locate multiple frogs in a dense chorus.

This paper presents a generalized multiple-point sparse constrained deconvolution approach for mapping acoustic noise sources in large regions using a movable array. Extended from our previous MPSC-DAMAS approach, we first derive a generalized inverse problem relating to the source powers and the array manifold using a generic beamformer and an explicit measurement noise model. We then propose a generalized MPSC-DAMAS (GMPSC-DAMAS) approach for resolving the inverse problem. A new parameter setting method based on a multiple-point minimum-variance-distortionless-response (MVDR) beamformer is also presented. The realizations of the GMPSC-DAMAS approach using the delay- and-sum (DAS) beamformer and the MVDR beamformer are evaluated. Simulation results show the proposed GMPSC-DAMAS approach achieves much lower absolute power estimation errors and processing time than the MPSC-DAMAS approach in terms of number of sources and robustness to measurement noise.

Current sensory array systems do not fully exploit tactile sensing strategies widely used by vibrissal sensing animals to explore their surroundings. We develop a new tactile fluid-flow imaging technique, which relates rat's whisker movements to tomographic imaging to extract fluid-flow characteristics with a robotic whisker array. At high Reynolds numbers, the drag force on a whisker segment is proportional to the relative velocity squared, and acts as a distributed load along the whisker length. Therefore, we propose that it is possible to map out the 2-D cross-sectional mean fluid-flow velocity field with a robotic whisker array by measuring the moment sensed at each whisker base from different directions for tomographic reconstruction. The associated inverse problem for the tomographic image formation is formulated as a MAP estimation problem and solved computationally. The experimental results demonstrate that this new approach offers a fundamentally novel sensor technology for flow-field measurements.

Existing techniques utilizing bio-inspired robotic whisker sensory systems generally address object feature extraction with artificial whiskers as a mechanical problem. We present an alternative signal-processing approach that formulates the object shape recognition as a 2-D tactile imaging problem. Observing that the whisker position at the very initial contact is similar to a `ray path' in X-ray computed tomography; the 2-D cross-sectional image of the object can be tomographically reconstructed by measuring only the angle at the whisker base and the whisker-base location at the initial contact for each viewing angle. This approach has the important practical advantage of eliminating the need for the calibration of the force/moment measurements. Experimental results demonstrate the promising potential for bio-inspired systems using arrays of vibrissal sensors for object shape recognition.

The main drawback of Orthogonal Frequency Division Multiplexing (OFDM) systems is the high peak-to-average power ratio (PAPR), which leads to a significant reduction in performance and power efficiency. Tone injection (TI) is a promising PAPR reduction technique that cyclically extends QAM constellations to allow an alternative encoding with lower PAPR at the transmitter. We present a new efficient complex-baseband algorithm which performs TI by using aggressive clipping to combat large peaks. An approximated-analog PAPR reduction of up to 5.1 dB at a 10-5 symbol-clip probability is obtained for 64-channel 16-QAM OFDM. This is a very fast and practical peak-power reduction method for OFDM systems that essentially achieves the same PAPR as single-carrier modulation for large constellations.