Inference of Room Geometry From Acoustic Impulse Responses

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Localization of planar acoustic reflectors from the combination of linear estimates

In this paper we present a simple yet effective method for estimating the geometry of an acoustic enclosure in three-dimensions. By capturing the acoustic impulse responses using a microphone array and a loudspeaker at different spatial locations we transform the localization of planar reflectors into the estimation of multiple linear reflectors. By decomposing the microphone array into co-planar sub-arrays the line parameters of the reflectors lying on the corresponding planes can be inferred using a geometric constraint. By intersecting these lines the actual lying plane of each reflector can be estimated. The proposed method is evaluated using a three-dimensional microphone array in a real conference room

Inferring Room Geometries

Determining the geometry of an acoustic enclosure using microphone arrays has become an active area of research. Knowledge gained about the acoustic environment, such as the location of reflectors, can be advantageous for applications such as sound source localization, dereverberation and adaptive echo cancellation by assisting in tracking environment changes and helping the initialization of such algorithms. A methodology to blindly infer the geometry of an acoustic enclosure by estimating the location of reflective surfaces based on acoustic measurements using an arbitrary array geometry is developed and analyzed. The starting point of this work considers a geometric constraint, valid both in two and three-dimensions, that converts time-of-arrival and time-difference-pf-arrival information into elliptical constraints about the location of reflectors. Multiple constraints are combined to yield the line or plane parameters of the reflectors by minimizing a specific cost function in the least-squares sense. An iterative constrained least-squares estimator, along with a closed-form estimator, that performs optimally in a noise-free scenario, solve the associated common tangent estimation problem that arises from the geometric constraint. Additionally, a Hough transform based data fusion and estimation technique, that considers acquisitions from multiple source positions, refines the reflector localization even in adverse conditions. An extension to the geometric inference framework, that includes the estimation of the actual speed of sound to improve the accuracy under temperature variations, is presented that also reduces the required prior information needed such that only relative microphone positions in the array are required for the localization of acoustic reflectors. Simulated and real-world experiments demonstrate the feasibility of the proposed method.Open Acces

Geometrical room geometry estimation from room impulse responses

© 2016 IEEE. Room geometry estimation from corresponding Room Impulse Responses (RIRs) has attracted much attention in recent years, and a key challenge is to find the first order image source locations from the RIRs under different environments. Unlike the existing approaches which require a priori knowledge of the room or require some ideal conditions, this paper proposes an intuitive geometrical method based on the acoustical image source model. The proposed approach does not need any a priori knowledge of the room, only the RIRs from one arbitrary source location to five arbitrary receiving locations. The first order image sources of the walls in a room are identified first, then the room geometry is estimated based on the wall locations using a geometrical approach. Simulations with 2D and 3D convex polyhedral rooms demonstrate the feasibility and the precision of the proposed approach is discussed

Raking the Cocktail Party

We present the concept of an acoustic rake receiver---a microphone beamformer that uses echoes to improve the noise and interference suppression. The rake idea is well-known in wireless communications; it involves constructively combining different multipath components that arrive at the receiver antennas. Unlike spread-spectrum signals used in wireless communications, speech signals are not orthogonal to their shifts. Therefore, we focus on the spatial structure, rather than temporal. Instead of explicitly estimating the channel, we create correspondences between early echoes in time and image sources in space. These multiple sources of the desired and the interfering signal offer additional spatial diversity that we can exploit in the beamformer design. We present several "intuitive" and optimal formulations of acoustic rake receivers, and show theoretically and numerically that the rake formulation of the maximum signal-to-interference-and-noise beamformer offers significant performance boosts in terms of noise and interference suppression. Beyond signal-to-noise ratio, we observe gains in terms of the \emph{perceptual evaluation of speech quality} (PESQ) metric for the speech quality. We accompany the paper by the complete simulation and processing chain written in Python. The code and the sound samples are available online at \url{http://lcav.github.io/AcousticRakeReceiver/}.Comment: 12 pages, 11 figures, Accepted for publication in IEEE Journal on Selected Topics in Signal Processing (Special Issue on Spatial Audio

Room boundary estimation from acoustic room impulse responses

Boundary estimation from an acoustic room impulse response (RIR), exploiting known sound propagation behavior, yields useful information for various applications: e.g., source separation, simultaneous localization and mapping, and spatial audio. The baseline method, an algorithm proposed by Antonacci et al., uses reflection times of arrival (TOAs) to hypothesize reflector ellipses. Here, we modify the algorithm for 3-D environments and for enhanced noise robustness: DYPSA and MUSIC for epoch detection and direction of arrival (DOA) respectively are combined for source localization, and numerical search is adopted for reflector estimation. Both methods, and other variants, are tested on measured RIR data; the proposed method performs best, reducing the estimation error by 30%

HETEROGENEOUS MULTI-SENSOR FUSION FOR 2D AND 3D POSE ESTIMATION

Sensor fusion is a process in which data from different sensors is combined to acquire an output that cannot be obtained from individual sensors. This dissertation first considers a 2D image level real world problem from rail industry and proposes a novel solution using sensor fusion, then proceeds further to the more complicated 3D problem of multi sensor fusion for UAV pose estimation. One of the most important safety-related tasks in the rail industry is an early detection of defective rolling stock components. Railway wheels and wheel bearings are two components prone to damage due to their interactions with the brakes and railway track, which makes them a high priority when rail industry investigates improvements to current detection processes. The main contribution of this dissertation in this area is development of a computer vision method for automatically detecting the defective wheels that can potentially become a replacement for the current manual inspection procedure. The algorithm fuses images taken by wayside thermal and vision cameras and uses the outcome for the wheel defect detection. As a byproduct, the process will also include a method for detecting hot bearings from the same images. We evaluate our algorithm using simulated and real data images from UPRR in North America and it will be shown in this dissertation that using sensor fusion techniques the accuracy of the malfunction detection can be improved. After the 2D application, the more complicated 3D application is addressed. Precise, robust and consistent localization is an important subject in many areas of science such as vision-based control, path planning, and SLAM. Each of different sensors employed to estimate the pose have their strengths and weaknesses. Sensor fusion is a known approach that combines the data measured by different sensors to achieve a more accurate or complete pose estimation and to cope with sensor outages. In this dissertation, a new approach to 3D pose estimation for a UAV in an unknown GPS-denied environment is presented. The proposed algorithm fuses the data from an IMU, a camera, and a 2D LiDAR to achieve accurate localization. Among the employed sensors, LiDAR has not received proper attention in the past; mostly because a 2D LiDAR can only provide pose estimation in its scanning plane and thus it cannot obtain full pose estimation in a 3D environment. A novel method is introduced in this research that enables us to employ a 2D LiDAR to improve the full 3D pose estimation accuracy acquired from an IMU and a camera. To the best of our knowledge 2D LiDAR has never been employed for 3D localization without a prior map and it is shown in this dissertation that our method can significantly improve the precision of the localization algorithm. The proposed approach is evaluated and justified by simulation and real world experiments

Physics, Astrophysics and Cosmology with Gravitational Waves

Gravitational wave detectors are already operating at interesting sensitivity levels, and they have an upgrade path that should result in secure detections by 2014. We review the physics of gravitational waves, how they interact with detectors (bars and interferometers), and how these detectors operate. We study the most likely sources of gravitational waves and review the data analysis methods that are used to extract their signals from detector noise. Then we consider the consequences of gravitational wave detections and observations for physics, astrophysics, and cosmology.Comment: 137 pages, 16 figures, Published version <http://www.livingreviews.org/lrr-2009-2

Proceedings of the 2011 Joint Workshop of Fraunhofer IOSB and Institute for Anthropomatics, Vision and Fusion Laboratory

This book is a collection of 15 reviewed technical reports summarizing the presentations at the 2011 Joint Workshop of Fraunhofer IOSB and Institute for Anthropomatics, Vision and Fusion Laboratory. The covered topics include image processing, optical signal processing, visual inspection, pattern recognition and classification, human-machine interaction, world and situation modeling, autonomous system localization and mapping, information fusion, and trust propagation in sensor networks