Sound Source Localization Using Time Differences of Arrival; Euclidean Distance Matrices Based Approach

Tehrani, Ali K. Z.; Makkiabadi, Bahador; Pourmohammad, Ali; Hozhabr, Seyed Hani

doi:10.1109/istel.2018.8661037

Cited by 6 publications

(2 citation statements)

References 9 publications

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“…The multipath component, together with the background noise, can lead to distortion of the time delay information from received signals, and thus it can degrade the localization performance. To address key challenges of the realistic environment such as room reverberation, background noise, and sound interference, different methods to compute the TDOAs across various combinations of pairs of spatially separated microphones were proposed [44]- [47]. Recent work also suggested that deep learning can successfully be applied for modeling rooms acoustic [48].…”

Section: A Related Workmentioning

confidence: 99%

Free-Field TDOA-AOA Sound Source Localization Using Three Soundfield Microphones

et al. 2020

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Accurate source localization is an important problem in many research areas as well as practical applications in wireless communications and acoustic signal processing. This paper presents a passive three-dimensional sound source localization (SSL) method that employs a geometric configuration of three soundfield microphones. Two methods for estimating the angle of arrival (AOA) and time difference of arrival (TDOA) are proposed based on Ambisonics A and B format signals. The closed-form solution for sound source location estimation based on two TDOAs and three AOAs is derived. The proposed method is evaluated by simulations and physical experiments in our anechoic chamber. Simulations demonstrate that the estimation method can theoretically obtain Cramér-Rao lower bound for a small Gaussian noise present in AOA and TDOA observations. Investigation on the uncertainty of TDOA and AOA measurements depending on the length of measurement interval is also conducted. Experimental results in terms of RMSE indicate that the proposed solution can be used to accurately find a 3D position of the sound source in free-field environment. Performance evaluation regarding the number of estimation steps shows that higher accuracy can be achieved by longer observations of stationary sound source. INDEX TERMS 3D sound source localization, angle of arrival, cramér-rao bound, soundfiled microphone, time difference of arrival.

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Section: A Related Workmentioning

confidence: 99%

Free-Field TDOA-AOA Sound Source Localization Using Three Soundfield Microphones

et al. 2020

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“…Additionally, researchers focused on a frequency difference of arrival (FDoA) [23] and the Doppler frequency shift (DFS) method [24]. Euclidean distance was adopted by Tehrani et al [25] along with the convex optimization approach, converting the numerical analysis problem of the functions used in the TDoA method to an iterative minimization problem. The second one is to incorporate information from different sources.…”

Section: Introductionmentioning

confidence: 99%

Adding ears to intelligent connected vehicles by combining microphone arrays and high definition map

Jiang

Yang

Wijaya

et al. 2021

IET Intelligent Trans Sys

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Ears are responsible for the sense of hearing, which is indispensable for a human to understand the environment. It is similar to an intelligent vehicle where acoustical data contains useful and valuable information to comprehend the environment to enable autonomous driving. One important application of acoustical data processing is to localize sound sources. However, due to the complexity of microphone array setup and the unstable localization performance against noises, few research articles tried to localize surrounding vehicles based on on-board microphone arrays. The main contribution is the first implementation to combine roof-mounted microphones and high definition maps as vehicle' s ears for online 3D localization of surround vehicles. This article designed a six-microphones sensor array dedicated to the on-board application and improved sound source localization algorithms using the observer technique to adapt to dynamic driving scenarios. A map-based localization enhancement method is also proposed to make the system more robust to noises and model errors. Feasibility tests are conducted with real vehicles in the scenarios of pass-by and overtaking. Experimental results validate the feasibility of using microphones to localize surrounding vehicles with lane-level accuracy. INTRODUCTIONAcoustical data contains useful and valuable information for the comprehension of the environment to enable autonomous driving. However, in the current research of intelligent connected vehicles (ICV), acoustical data processing is not valued as much as other perception techniques. The most widely employed sensors in ICV are microwave radar [1], LiDAR [2], and cameras [3]. These sensors can provide precise and detailed information of the traffic scene for localization and detection. For this reason, these sensors are also called the eyes of ICV. Nevertheless, in some situations, the eyes could be temporarily blind and cause road safety disasters. For example, LiDAR is sensitive to rainy days, while cameras can be influenced by fog, smoke, and dirt on the lens. To address these problems, plenty of sensor fusion methods were proposed to compensate for the limitations of using a single sensor. [4]. Filter-based methods, such as EKF [5] and UKF [6], combine inertia and visual information. [7] and [8] This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Sound source localization of harmonic sources in entire 3D space using just 5 acoustic signals

Thakur

Singh

2022

Applied Acoustics

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Sound Source Localization Using Time Differences of Arrival; Euclidean Distance Matrices Based Approach

Cited by 6 publications

References 9 publications

Free-Field TDOA-AOA Sound Source Localization Using Three Soundfield Microphones

Free-Field TDOA-AOA Sound Source Localization Using Three Soundfield Microphones

Adding ears to intelligent connected vehicles by combining microphone arrays and high definition map

Sound source localization of harmonic sources in entire 3D space using just 5 acoustic signals

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