Simultaneous asynchronous microphone array calibration and sound source localisation

Su, Daobilige; Vidal-Calleja, Teresa; Miró, Jaime Valls

doi:10.1109/iros.2015.7354165

Cited by 11 publications

(4 citation statements)

References 13 publications

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“…Figure 9 shows the principle of the optimal hyperplane and the optimal margin in SVM modeling. The discriminant function of the SVM is given by: ƒ(x) = ∑ N i=1 α i t i K(x, x i ) + d (23) where the t i are the ideal outputs, ∑ N i=1 α i t i = 0, and α i > 0. The vectors x i are support vectors and are obtained from the training set by an optimization process.…”

Section: Gaussian Mixture Modelmentioning

confidence: 99%

“…Computational auditory scene analysis (CASA) has progressed in understanding environmental sounds, focusing on source localization and separation [16,21,22]. In contrast, audio-based SLAM algorithms are relatively less mature and face certain challenges that may hinder their widespread adoption in robotics [23,24]. Audio-based SLAM typically involves several important steps.…”

Section: Introductionmentioning

confidence: 99%

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An Audio-Based SLAM for Indoor Environments: A Robotic Mixed Reality Presentation

Lahemer,

Rad

2024

Sensors

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In this paper, we present a novel approach referred to as the audio-based virtual landmark-based HoloSLAM. This innovative method leverages a single sound source and microphone arrays to estimate the voice-printed speaker’s direction. The system allows an autonomous robot equipped with a single microphone array to navigate within indoor environments, interact with specific sound sources, and simultaneously determine its own location while mapping the environment. The proposed method does not require multiple audio sources in the environment nor sensor fusion to extract pertinent information and make accurate sound source estimations. Furthermore, the approach incorporates Robotic Mixed Reality using Microsoft HoloLens to superimpose landmarks, effectively mitigating the audio landmark-related issues of conventional audio-based landmark SLAM, particularly in situations where audio landmarks cannot be discerned, are limited in number, or are completely missing. The paper also evaluates an active speaker detection method, demonstrating its ability to achieve high accuracy in scenarios where audio data are the sole input. Real-time experiments validate the effectiveness of this method, emphasizing its precision and comprehensive mapping capabilities. The results of these experiments showcase the accuracy and efficiency of the proposed system, surpassing the constraints associated with traditional audio-based SLAM techniques, ultimately leading to a more detailed and precise mapping of the robot’s surroundings.

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Section: Gaussian Mixture Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Audio-Based SLAM for Indoor Environments: A Robotic Mixed Reality Presentation

Lahemer,

Rad

2024

Sensors

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“…Results using HARK and three static robots with 8-MAs in an anechoic chamber and two moving talkers are provided. Audio-based SLAM has also been used for online calibration of asynchronous MAs [24], [25] and for optimizing the relative positions of multiple mobile robots with MAs for cooperative sound source separation [26].…”

Section: Related Workmentioning

confidence: 99%

3D Localization of a Sound Source Using Mobile Microphone Arrays Referenced by SLAM

Michaud

Faucher

Grondin

et al. 2020

2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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A microphone array can provide a mobile robot with the capability of localizing, tracking and separating distant sound sources in 2D, i.e., estimating their relative elevation and azimuth. To combine acoustic data with visual information in real world settings, spatial correlation must be established. The approach explored in this paper consists of having two robots, each equipped with a microphone array, localizing themselves in a shared reference map using SLAM. Based on their locations, data from the microphone arrays are used to triangulate in 3D the location of a sound source in relation to the same map. This strategy results in a novel cooperative sound mapping approach using mobile microphone arrays. Trials are conducted using two mobile robots localizing a static or a moving sound source to examine in which conditions this is possible. Results suggest that errors under 0.3 m are observed when the relative angle between the two robots are above 30 • for a static sound source, while errors under 0.3 m for angles between 40 • and 140 • are observed with a moving sound source.

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“…Another problem in cooperative source separation is that the synchronization between each robot and the positions of each sound source and robot are necessary. Although these are difficult to estimate without a special device such as a GPS, several studies have explored the feasibility of estimating them simultaneously without a special device by using a SLAM framework [6,7].…”

Section: Introductionmentioning

confidence: 99%

Layout Optimization of Cooperative Distributed Microphone Arrays Based on Estimation of Source Separation Performance

Sekiguchi¹,

Bando²,

Itoyama³

et al. 2017

J. Robot. Mechatron.

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[abstFig src='/00290001/08.jpg' width='300' text='Optimizing robot positions for source separation' ] The active audition method presented here improves source separation performance by moving multiple mobile robots to optimal positions. One advantage of using multiple mobile robots that each has a microphone array is that each robot can work independently or as part of a big reconfigurable array. To determine optimal layout of the robots, we must be able to predict source separation performance from source position information because actual source signals are unknown and actual separation performance cannot be calculated. Our method thus simulates delay-and-sum beamforming from a possible layout to calculate gain theoretically, i.e., the expected ratio of a target sound source to other sound sources in the corresponding separated signal. Robots are moved into the layout with the highest average gain over target sources. Experimental results showed that our method improved the harmonic mean of signal-to-distortion ratios (SDRs) by 5.5 dB in simulation and by 3.5 dB in a real environment.

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Simultaneous asynchronous microphone array calibration and sound source localisation

Cited by 11 publications

References 13 publications

An Audio-Based SLAM for Indoor Environments: A Robotic Mixed Reality Presentation

An Audio-Based SLAM for Indoor Environments: A Robotic Mixed Reality Presentation

3D Localization of a Sound Source Using Mobile Microphone Arrays Referenced by SLAM

Layout Optimization of Cooperative Distributed Microphone Arrays Based on Estimation of Source Separation Performance

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