First order echo based room shape recovery using a single mobile device

Wang, Tiexing; Peng, Fangrong; Chen, Biao

doi:10.1109/icassp.2016.7471629

Cited by 9 publications

(13 citation statements)

References 8 publications

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“…The main application for this manuscript is acoustic reflector mapping for robot audition. For this application, the mapping should be possible in unknown, complex environments, and we therefore do not rely on trivial room geometry models as opposed to many of the traditional methods for room geometry estimation [10][11][12]. Therefore, we chose to use a small number of reflections in the estimation (i.e., R = 3), to mainly estimate the TOAs/DOAs of first-order reflections impinging from nearby acoustic reflectors.…”

Section: Comparison Of With State-of-the-artmentioning

confidence: 99%

“…A similar approach also based on a priori RIR/TOA knowledge was considered using a multichannel setup in the context of robotics in [11]. Other authors considered an approach where the TOAs of the first order echoes are utilized for estimating the arbitrary convex room shapes [12]. As briefly mentioned, these as well as other active approaches do not consider the TOA estimation problem, which is an equally important and difficult problem in itself due to, e.g., spurious estimates [13].…”

Section: Introductionmentioning

confidence: 99%

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Estimation of acoustic echoes using expectation-maximization methods

Saqib

Gannot

Jensen

2020

J AUDIO SPEECH MUSIC PROC.

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Estimation problems like room geometry estimation and localization of acoustic reflectors are of great interest and importance in robot and drone audition. Several methods for tackling these problems exist, but most of them rely on information about times-of-arrival (TOAs) of the acoustic echoes. These need to be estimated in practice, which is a difficult problem in itself, especially in robot applications which are characterized by high ego-noise. Moreover, even if TOAs are successfully extracted, the difficult problem of echolabeling needs to be solved. In this paper, we propose multiple expectation-maximization (EM) methods, for jointly estimating the TOAs and directions-of-arrival (DOA) of the echoes, with a uniform circular array (UCA) and a loudspeaker in its center for probing the environment. The different methods are derived to be optimal under different noise conditions. The experimental results show that the proposed methods outperform existing methods in terms of estimation accuracy in noisy conditions. For example, it can provide accurate estimates at SNR of 10 dB lower compared to TOA extraction from room impulse responses, which is often used. Furthermore, the results confirm that the proposed methods can account for scenarios with colored noise or faulty microphones. Finally, we show the applicability of the proposed methods in mapping of an indoor environment.

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Section: Comparison Of With State-of-the-artmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Estimation of acoustic echoes using expectation-maximization methods

Saqib

Gannot

Jensen

2020

J AUDIO SPEECH MUSIC PROC.

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“…Some approaches use several microphones that are distributed in the space [4] or a spherical microphone array [15,16], some combine them with panoramic cameras [17]. Some methods [9,11,18,19] even work with smartphones to derive the geometry of polygon-shaped rooms.…”

Section: Introductionmentioning

confidence: 99%

(W)Earable Microphone Array and Ultrasonic Echo Localization for Coarse Indoor Environment Mapping

Pfreundtner

Yang

Sörös

2021

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

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We present a microphone array structure for spherical sound incidence angle tracking that can be attached to headphones or directly integrated into earphones. We show that this microphone array together with an ultrasonic sound source, e.g., a home assistant speaker in the room, allows to estimate the direction and distance of sound reflections on wall surfaces in the room. With our presented method, we achieved sound incidence angle estimation errors of around 14 • in the horizontal plane and around 5 • in the vertical plane, and reflection position estimation errors of around 5 cm within 2 m. Having the reflection points in 3D allows us to sparsely capture the closest room geometry at interactive rate, which is a necessary step towards indoor audio augmented reality applications.

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“…However, this prerequisite is fundamentally conflicting with acoustic signals, affected by speech inactivity and reverberation. The second category [21], [22] localizes an active acoustic observer, equipped with a loudspeaker for actively probing the room and microphones for Room Impulse Response (RIR) measurements, and estimates the room dimensions from the TOAs of early reflections. However, TOA estimation from RIRs is ambiguous [23], and emission of controlled sound stimuli for RIR measurements is highly intrusive to people nearby, and therefore unacceptable in many important use-cases.…”

Section: Introductionmentioning

confidence: 99%

Acoustic SLAM

Evers

Naylor

2018

IEEE/ACM Trans. Audio Speech Lang. Process.

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An algorithm is presented that enables devices equipped with microphones, such as robots, to move within their environment in order to explore, adapt to, and interact with sound sources of interest. Acoustic scene mapping creates a threedimensional (3D) representation of the positional information of sound sources across time and space. In practice, positional source information is only provided by Direction-of-Arrival (DoA) estimates of the source directions; the source-sensor range is typically difficult to obtain. DoA estimates are also adversely affected by reverberation, noise, and interference, leading to errors in source location estimation and consequent false DoA estimates. Moreover, many acoustic sources, such as human talkers, are not continuously active, such that periods of inactivity lead to missing DoA estimates. Withal, the DoA estimates are specified relative to the observer's sensor location and orientation. Accurate positional information about the observer therefore is crucial. This paper proposes Acoustic Simultaneous Localization and Mapping (aSLAM), which uses acoustic signals to simultaneously map the 3D positions of multiple sound sources while passively localizing the observer within the scene map. The performance of aSLAM is analyzed and evaluated using a series of realistic simulations. Results are presented to show the impact of the observer motion and sound source localization accuracy.

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First order echo based room shape recovery using a single mobile device

Cited by 9 publications

References 8 publications

Estimation of acoustic echoes using expectation-maximization methods

Estimation of acoustic echoes using expectation-maximization methods

(W)Earable Microphone Array and Ultrasonic Echo Localization for Coarse Indoor Environment Mapping

Acoustic SLAM

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