A 3D model for room boundary estimation

Remaggi, Luca; Jackson, Philip J. B.; Wang, Wenwu; Chambers, Jonathon A.

doi:10.1109/icassp.2015.7178022

Cited by 12 publications

(29 citation statements)

References 22 publications

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“…In the 1960's, Kac famously lectured about a solution for solving the age-old physics problem of estimating the geometry of a drum based on the sound generated from striking the surface. More recently, this problem has been extended to estimating the shape of rooms based on their acoustic RIR [3,8,11,17,24,30,31,36]. Acoustic geometry reconstruction typically assumes a set of microphones and speaker arrays with known locations.…”

Section: Related Workmentioning

confidence: 99%

“…Most approaches rely on measuring the RIR and ind the most likely location of walls based on the signals' time of arrival (TOA) [8,11,17,30,31,36] or time diference of arrival (TDOA) [3,24] of impulses, depending on whether the speakers and microphones are synchronized. One approach models walls as planar surfaces tangent to the ellipsoid deined by the distance between transmitter/receiver pairs [3,31]. To ind the overlaps among multiple ellipsoids derived from noisy measurements, most techniques adopt Hough transform or RANSAC to reliably and eiciently ind the best solution.…”

Section: Related Workmentioning

confidence: 99%

“…In the early 1900's, Sabine began to model the impact of people, frequency and the geometry of spaces on acoustics [32]. Signiicant follow-on work has explored modeling sound in indoor spaces to the point where it is possible to use the arrival time of echoes relected of of walls to reconstruct room geometry [3,8,11,17,24,30,31,36]. These approaches leverage the RIR to ind the most likely position of walls in space using a set of speakers and microphones in a ixed and well-known coniguration.…”

Section: Introductionmentioning

confidence: 99%

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Can a phone hear the shape of a room?

Shih

Rowe

2019

Proceedings of the 18th International Conference on Information Processing in Sensor Networks

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Understanding the location of acoustically relective surfaces in a room is a critical component in advanced sound processing. For example, intelligent speakers can use a room's acoustic geometry to improve playback quality, source separation accuracy, and speech recognition. In this paper, we present Synesthesia, a system for capturing the acoustic properties of a room using a single ixed speaker and a mobile phone that records audio at multiple locations. Using the arrival time of echoes, the system is able to reconstruct the position of relective surfaces like walls and then estimate properties like surface absorption. Previous work has shown how the acoustic room impulse response (RIR) of an environment can be used to analyze echoes within a space to reconstruct room geometry. The best current RIR-based approaches rely on high-end equipment and capturing an acoustic signal broadcast into space from a known ixed constellation of microphones. They also require the precise calibration and measurement of microphone positions. In addition, most approaches pose constraints on room geometries and limit the order of RIR to achieve accurate and consistent results. In this paper, we introduce a new approach that performs RIR imaging using a mobile phone that tracks its location with visual inertial odometry (VIO) to record a dense set of samples albeit with noise in their locations. We present a new approach that is able to relax several key assumptions on RIR and show through both experimentation and simulation that even with 20cm of uncertainty in the microphone locations provided by VIO, we are still able to reconstruct the room geometry with accurate shape and dimensions. We demonstrate this capability by prototyping a tool for acoustic engineers, that allows a user to view a room's estimated geometry and absorption overlaid on the actual sensed space with augmented reality. CCS CONCEPTS • Theory of computation → Nonconvex optimization; Unsupervised learning and clustering; • Human-centered computing → Mobile computing;

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Can a phone hear the shape of a room?

Shih

Rowe

2019

Proceedings of the 18th International Conference on Information Processing in Sensor Networks

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“…In addition, the reflector search was computationally expensive, caused mainly by the optimization of its cost function. In our previous work [36], a full 3D method was proposed, exploiting direct reconstruction through ellipsoids. The reflector search was performed utilizing two variants, either the random sample consensus (RANSAC) algorithm [37], or the combination of the cost function used in [29] and the Hough transform.…”

Section: Introductionmentioning

confidence: 99%

“…• a multichannel version of DYPSA [32], i.e. clustered DYPSA (C-DYPSA), to automatically extract reflection TOAs from compact microphone array RIRs; • the image-source reversion method ISDAR-LIB, created by the fusion of our ISDAR (the image-source locator presented in [30]) and loudspeaker-image bisection (LIB) (a reflector localization algorithm in [24], [25]); • two further novel variants of ISDAR-LIB, exploiting multiple loudspeakers; • ellipsoid tangent sample consensus (ETSAC), a direct localization method (modified from [36], by utilizing the new C-DYPSA instead of DYPSA); • a comparative evaluation of the state-of-the-art and the proposed methods, using synthetic and measured RIRs. The comprehensive comparison presented here is, to our knowledge, the first that compares image-source reversion and direct localization methods, as approaches for 3D reflector localization.…”

Section: Introductionmentioning

confidence: 99%

Acoustic Reflector Localization: Novel Image Source Reversion and Direct Localization Methods

Remaggi

Jackson

Coleman

et al. 2017

IEEE/ACM Trans. Audio Speech Lang. Process.

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Abstract-Acoustic reflector localization is an important issue in audio signal processing, with direct applications in spatial audio, scene reconstruction, and source separation. Several methods have recently been proposed to estimate the 3D positions of acoustic reflectors given room impulse responses (RIRs). In this article, we categorize these methods as "image-source reversion", which localizes the image source before finding the reflector position, and "direct localization", which localizes the reflector without intermediate steps. We present five new contributions. First, an onset detector, called the clustered dynamic programming projected phase-slope algorithm, is proposed to automatically extract the time of arrival for early reflections within the RIRs of a compact microphone array. Second, we propose an image-source reversion method that uses the RIRs from a single loudspeaker. It is constructed by combining an image source locator (the image source direction and range (ISDAR) algorithm), and a reflector locator (using the loudspeaker-image bisection (LIB) algorithm). Third, two variants of it, exploiting multiple loudspeakers, are proposed. Fourth, we present a direct localization method, the ellipsoid tangent sample consensus (ETSAC), exploiting ellipsoid properties to localize the reflector. Finally, systematic experiments on simulated and measured RIRs are presented, comparing the proposed methods with the state-of-the-art. ETSAC generates errors lower than the alternative methods compared through our datasets. Nevertheless, the ISDAR-LIB combination performs well and has a run time 200 times faster than ETSAC.

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Prediction of Object Geometry from Acoustic Scattering Using Convolutional Neural Networks

Fan

Vineet

et al. 2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Acoustic scattering is strongly influenced by boundary geometry of objects over which sound scatters. The present work proposes a method to infer object geometry from scattering features by training convolutional neural networks. The training data is generated from a fast numerical solver developed on CUDA. The complete set of simulations is sampled to generate multiple datasets containing different amounts of channels and diverse image resolutions. The robustness of our approach in response to data degradation is evaluated by comparing the performance of networks trained using the datasets with varying levels of data degradation. The present work has found that the predictions made from our models match ground truth with high accuracy. In addition, accuracy does not degrade when fewer data channels or lower resolutions are used.

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A 3D model for room boundary estimation

Cited by 12 publications

References 22 publications

Can a phone hear the shape of a room?

Can a phone hear the shape of a room?

Acoustic Reflector Localization: Novel Image Source Reversion and Direct Localization Methods

Prediction of Object Geometry from Acoustic Scattering Using Convolutional Neural Networks

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