Localization of multiple acoustic sources with small arrays using a coherence test

Mohan, Sibin; Lockwood, Michael E.; Kramer, Michael L.; Jones, Douglas L.

doi:10.1121/1.2871597

Cited by 108 publications

(112 citation statements)

References 18 publications

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“…It does this by projecting the received signals in a DOA subspace based on their eigenvectors, similar to principal component analysis. It was applied in [14] with good results, although it has been observed that its performance decreases considerably in the presence of reverberation [38] (pp. 169).…”

Section: Challenges In Estimating Multiple Doas In a Mobile Robotic Pmentioning

confidence: 99%

Integration of the Multi-DOA Estimation Functionality to Human-Robot Interaction

Rascón

Meza

Fuentes

et al. 2015

International Journal of Advanced Robotic Systems

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Sound source localization is important in human interaction, such as in locating the origin of long-distance calls or facing other humans while in a conversation. It is of interest to apply such functionality to the core of human-robot interaction (HRI) and investigate its benefits, if any. In this paper, we propose three strategies for how to integrate the functionality of multiple directions-of-arrival (multi-DOA) estimation with a common scenario, in which the robot acts as a waiter while applying audio source localization. The proposed strategies are: a) the robot locates calls from users at a relatively long distance; b) the robot faces the user when taking the order; and c) the robot announces whether the acoustic environment is not conducive to understanding a speech command (mainly where more than one user speaks at once). It was seen that users react favourably to the functionality, and that it even has a noticeable influence on the success of the interaction.

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Section: Challenges In Estimating Multiple Doas In a Mobile Robotic Pmentioning

confidence: 99%

Integration of the Multi-DOA Estimation Functionality to Human-Robot Interaction

Rascón

Meza

Fuentes

et al. 2015

International Journal of Advanced Robotic Systems

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“…It does this by projecting the received signals in a DOA subspace, based on their eigenvectors, similar to Principal Component Analysis. It was applied in [9] with good results, although it has been observed that its performance decreases considerably in the presence of reverberation [21] (pp. 169).…”

Section: On Source Direction Estimationmentioning

confidence: 99%

Robotic Orientation towards Speaker for Human-Robot Interaction

Rascón

Avilés

Pineda

2010

Advances in Artificial Intelligence – IBERAMIA 2010

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Abstract. The orientation of conversational robots to face their interlocutors is essential for natural and efficient Human-Robot Interaction (HRI). In this paper, progress towards this objective is presented: a service robot able to detect the direction of a user, and orient itself towards him/her, in a complex auditive environment, using only voice and a 3-microphone system. This functionality is integrated within Spoken HRI using dialogue models and a cognitive architecture. The paper further discusses applications where robotic orientation benefits HRI, such as a tour-guide robot capable to guide a poster session and a "Marco Polo" game where a robot aims to follow a user purely by sound.

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“…However, coherent reflections are not independent and so the effective rank is less than the total number of sources and reflections. To improve the identification of TF-regions whose effective rank is one, the coherence test proposed in [7] averages the correlation matrix over time. The Direct-Path Dominance (DPD) test [8] leads to further improvement using frequency smoothing by also averaging the correlation matrix over frequency thus decorrelating coherent reflections.…”

mentioning

confidence: 99%

Direction of arrival estimation using pseudo-intensity vectors with direct-path dominance test

Moore

Evers

Naylor

et al. 2015

2015 23rd European Signal Processing Conference (EUSIPCO)

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The accuracy of direction of arrival estimation tends to degrade under reverberant conditions due to the presence of reflected signal components which are correlated with the direct path. The recently proposed direct-path dominance test provides a means of identifying time-frequency regions in which a single signal path is dominant. By analysing only these regions it was shown that the accuracy of the FS-MUSIC algorithm could be significantly improved. However, for real-time implementation a less computationally demanding localisation algorithm would be preferable. In the present contribution we investigate the direct-path dominance test as a preprocessing step to pseudo-intensity vector-based localisation. A novel formulation of the pseudo-intensity vector is proposed which further exploits the direct path dominance test and leads to improved localisation performance.Index Terms-direction of arrival estimation, spherical harmonic domain, pseudo-intensity vectors BACKGROUNDDirection-of-Arrival (DOA) estimation (also known as bearingonly source localisation) is a fundamental problem in acoustic signal processing, particularly as a preprocessing step for beamforming and speech dereverberation [1]. In the context of robot audition it is important for estimation to be computationally efficient such that source localisation can be performed in real time and with low latency on relatively low cost hardware. In this paper we assume a spherical microphone array such that processing can be performed in the Spherical Harmonic Domain (SHD). This is advantageous because beampatterns can be created which are independent of the look direction making the system equally adept at localising sources to the rear and above as those to the front.Localisation using Steered Response Power (SRP) measures the output power from a beamformer as it is steered in turn at a grid of possible source directions and selects one or more maxima as DOA(s). Under anechoic conditions the For MUSIC [6] the noise space of the correlation matrix is used to evaluate the spatial spectrum for a grid of possible source directions. The effective rank of the correlation matrix yields the number of independent sources. In principle, exploiting the sparsity of speech, one can perform accurate DOA estimation for multiple sources by computing the MUSIC spectrum independently for each TF-region whose correlation matrix has effective rank one. However, coherent reflections are not independent and so the effective rank is less than the total number of sources and reflections. To improve the identification of TF-regions whose effective rank is one, the coherence test proposed in [7] averages the correlation matrix over time. The Direct-Path Dominance (DPD) test [8] leads to further improvement using frequency smoothing by also averaging the correlation matrix over frequency thus decorrelating coherent reflections.In contrast to SRP and MUSIC, the Pseudo-Intensity Vector (PIV) approach to DOA estimation [9] does not evaluate a cost function over a grid of poss...

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Localization of multiple acoustic sources with small arrays using a coherence test

Cited by 108 publications

References 18 publications

Integration of the Multi-DOA Estimation Functionality to Human-Robot Interaction

Integration of the Multi-DOA Estimation Functionality to Human-Robot Interaction

Robotic Orientation towards Speaker for Human-Robot Interaction

Direction of arrival estimation using pseudo-intensity vectors with direct-path dominance test

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