28,861 research outputs found
Acoustic simultaneous localization and mapping (A-SLAM) of a moving microphone array and its surrounding speakers
Acoustic scene mapping creates a representation of positions of audio sources such as talkers within the surrounding environment of a microphone array. By allowing the array to move, the acoustic scene can be explored in order to improve the map. Furthermore, the spatial diversity of the kinematic array allows for estimation of the source-sensor distance in scenarios where source directions of arrival are measured. As sound source localization is performed relative to the array position, mapping of acoustic sources requires knowledge of the absolute position of the microphone array in the room. If the array is moving, its absolute position is unknown in practice. Hence, Simultaneous Localization and Mapping (SLAM) is required in order to localize the microphone array position and map the surrounding sound sources. In realistic environments, microphone arrays receive a convolutive mixture of direct-path speech signals, noise and reflections due to reverberation. A key challenge of Acoustic SLAM (a-SLAM) is robustness against reverberant clutter measurements and missing source detections. This paper proposes a novel bearing-only a-SLAM approach using a Single-Cluster Probability Hypothesis Density filter. Results demonstrate convergence to accurate estimates of the array trajectory and source positions
Towards Informative Path Planning for Acoustic SLAM
Acoustic scene mapping is a challenging task as microphone arrays can often localize sound sources only in terms of their directions. Spatial diversity can be exploited constructively to infer source-sensor range when using microphone arrays installed on moving platforms, such as robots. As the absolute location of a moving robot is often unknown in practice, Acoustic Simultaneous Localization And Mapping (a-SLAM) is required in order to localize the moving robotâs positions and jointly map the sound sources. Using a novel a-SLAM approach, this paper investigates the impact of the choice of robot paths on source mapping accuracy. Simulation results demonstrate that a-SLAM performance can be improved by informatively planning robot paths
Look, no Beacons! Optimal All-in-One EchoSLAM
We study the problem of simultaneously reconstructing a polygonal room and a
trajectory of a device equipped with a (nearly) collocated omnidirectional
source and receiver. The device measures arrival times of echoes of pulses
emitted by the source and picked up by the receiver. No prior knowledge about
the device's trajectory is required. Most existing approaches addressing this
problem assume multiple sources or receivers, or they assume that some of these
are static, serving as beacons. Unlike earlier approaches, we take into account
the measurement noise and various constraints on the geometry by formulating
the solution as a minimizer of a cost function similar to \emph{stress} in
multidimensional scaling. We study uniqueness of the reconstruction from
first-order echoes, and we show that in addition to the usual invariance to
rigid motions, new ambiguities arise for important classes of rooms and
trajectories. We support our theoretical developments with a number of
numerical experiments.Comment: 5 pages, 6 figures, submitted to Asilomar Conference on Signals,
Systems, and Computers Websit
Shapes from Echoes: Uniqueness from Point-to-Plane Distance Matrices
We study the problem of localizing a configuration of points and planes from
the collection of point-to-plane distances. This problem models simultaneous
localization and mapping from acoustic echoes as well as the notable "structure
from sound" approach to microphone localization with unknown sources. In our
earlier work we proposed computational methods for localization from
point-to-plane distances and noted that such localization suffers from various
ambiguities beyond the usual rigid body motions; in this paper we provide a
complete characterization of uniqueness. We enumerate equivalence classes of
configurations which lead to the same distance measurements as a function of
the number of planes and points, and algebraically characterize the related
transformations in both 2D and 3D. Here we only discuss uniqueness;
computational tools and heuristics for practical localization from
point-to-plane distances using sound will be addressed in a companion paper.Comment: 13 pages, 13 figure
Reflection-Aware Sound Source Localization
We present a novel, reflection-aware method for 3D sound localization in
indoor environments. Unlike prior approaches, which are mainly based on
continuous sound signals from a stationary source, our formulation is designed
to localize the position instantaneously from signals within a single frame. We
consider direct sound and indirect sound signals that reach the microphones
after reflecting off surfaces such as ceilings or walls. We then generate and
trace direct and reflected acoustic paths using inverse acoustic ray tracing
and utilize these paths with Monte Carlo localization to estimate a 3D sound
source position. We have implemented our method on a robot with a cube-shaped
microphone array and tested it against different settings with continuous and
intermittent sound signals with a stationary or a mobile source. Across
different settings, our approach can localize the sound with an average
distance error of 0.8m tested in a room of 7m by 7m area with 3m height,
including a mobile and non-line-of-sight sound source. We also reveal that the
modeling of indirect rays increases the localization accuracy by 40% compared
to only using direct acoustic rays.Comment: Submitted to ICRA 2018. The working video is available at
(https://youtu.be/TkQ36lMEC-M
Quantitative flaw characterization with scanning laser acoustic microscopy
Surface roughness and diffraction are two factors that have been observed to affect the accuracy of flaw characterization with scanning laser acoustic microscopy. In accuracies can arise when the surface of the test sample is acoustically rough. It is shown that, in this case, Snell's law is no longer valid for determining the direction of sound propagation within the sample. The relationship between the direction of sound propagation within the sample, the apparent flaw depth, and the sample's surface roughness is investigated. Diffraction effects can mask the acoustic images of minute flaws and make it difficult to establish their size, depth, and other characteristics. It is shown that for Fraunhofer diffraction conditions the acoustic image of a subsurface defect corresponds to a two-dimensional Fourier transform. Transforms based on simulated flaws are used to infer the size and shape of the actual flaw
Foreground-Background Ambient Sound Scene Separation
Ambient sound scenes typically comprise multiple short events occurring on
top of a somewhat stationary background. We consider the task of separating
these events from the background, which we call foreground-background ambient
sound scene separation. We propose a deep learning-based separation framework
with a suitable feature normaliza-tion scheme and an optional auxiliary network
capturing the background statistics, and we investigate its ability to handle
the great variety of sound classes encountered in ambient sound scenes, which
have often not been seen in training. To do so, we create single-channel
foreground-background mixtures using isolated sounds from the DESED and
Audioset datasets, and we conduct extensive experiments with mixtures of seen
or unseen sound classes at various signal-to-noise ratios. Our experimental
findings demonstrate the generalization ability of the proposed approach
Omnidirectional Bats, Point-to-Plane Distances, and the Price of Uniqueness
We study simultaneous localization and mapping with a device that uses
reflections to measure its distance from walls. Such a device can be realized
acoustically with a synchronized collocated source and receiver; it behaves
like a bat with no capacity for directional hearing or vocalizing. In this
paper we generalize our previous work in 2D, and show that the 3D case is not
just a simple extension, but rather a fundamentally different inverse problem.
While generically the 2D problem has a unique solution, in 3D uniqueness is
always absent in rooms with fewer than nine walls. In addition to the complete
characterization of ambiguities which arise due to this non-uniqueness, we
propose a robust solution for inexact measurements similar to analogous results
for Euclidean Distance Matrices. Our theoretical results have important
consequences for the design of collocated range-only SLAM systems, and we
support them with an array of computer experiments.Comment: 5 pages, 8 figures, submitted to ICASSP 201
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