Single-channel indoor microphone localization

Parhizkar, Reza; Vetterli, Martin

doi:10.1109/icassp.2014.6853834

Cited by 25 publications

(13 citation statements)

References 14 publications

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“…Finding the location of microphones is a well-covered topic in the literature. For example [46] deals with finding the location of a microphone utilizing a single loudspeaker and the room known shape. Array constellation calibration has been analyzed from a theoretical point of view for far-field [47] and near-field scenarios [48].…”

Section: Array Calibrationmentioning

confidence: 99%

Joint speaker localization and array calibration using expectation-maximization

Dorfan

Schwartz

Gannot

2020

J AUDIO SPEECH MUSIC PROC.

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Ad hoc acoustic networks comprising multiple nodes, each of which consists of several microphones, are addressed. From the ad hoc nature of the node constellation, microphone positions are unknown. Hence, typical tasks, such as localization, tracking, and beamforming, cannot be directly applied. To tackle this challenging joint multiple speaker localization and array calibration task, we propose a novel variant of the expectation-maximization (EM) algorithm. The coordinates of multiple arrays relative to an anchor array are blindly estimated using naturally uttered speech signals of multiple concurrent speakers. The speakers' locations, relative to the anchor array, are also estimated. The inter-distances of the microphones in each array, as well their orientations, are assumed known, which is a reasonable assumption for many modern mobile devices (in outdoor and in a several indoor scenarios). The well-known initialization problem of the batch EM algorithm is circumvented by an incremental procedure, also derived here. The proposed algorithm is tested by an extensive simulation study.

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Section: Array Calibrationmentioning

confidence: 99%

Joint speaker localization and array calibration using expectation-maximization

Dorfan

Schwartz

Gannot

2020

J AUDIO SPEECH MUSIC PROC.

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“…Based on specific technology, it is possible to categorize methodologies for smartphone-based indoor pedestrian self-positioning systems into two distinct groups: (1) infrastructure-based systems that use auxiliary equipment or a cooperation between nodes to realize target tracking [1,2,3,4,5,6,7,8,9], and (2) the infrastructure-free systems that realize pedestrian self-positioning using only the information provided by the smartphone carried on one’s person [9,10,11,12,13,14,15,16,17,18,19,20]. However, when using the former, the pedestrian is likely to experience difficulties in position acquisition when the cooperative information is unavailable.…”

Section: Introductionmentioning

confidence: 99%

Indoor Pedestrian Self-Positioning Based on Image Acoustic Source Impulse Using a Sensor-Rich Smartphone

Song

Wang

Qiu

et al. 2018

Sensors

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The ubiquity of sensor-rich smartphones provides opportunities for a low-cost method to track indoor pedestrians. In this situation, pedestrian dead reckoning (PDR) is a widely used technology; however, its cumulative error seriously affects its accuracy. This paper presents a method of combining infrastructure-free indoor acoustic self-positioning with PDR self-positioning, which verifies the rationality of PDR results through the acoustic constraint between a sound source and its image sources. We further determine the first-order echo delay measurements, thus obtaining the mobile user position. We verify that the proposed method can achieve a continuous self-positioning median error of 0.19 m, and the error probability below 0.12 m is 54.46%, which indicates its ability to eliminate PDR error, as well as its adaptability to environmental disturbances.

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“…The image model as described above indicates a duality between the image of source and microphones to model the multipath propagation [21]. Indeed, the observation of the source signal in a reverberant environment can be characterized as a collective observation of multiple microphones recording the direct-path propagation of a single source.…”

Section: B Image Microphone Modelmentioning

confidence: 99%

Spatial Sound Localization via Multipath Euclidean Distance Matrix Recovery

Taghizadeh

Asaei

Haghighatshoar

et al. 2015

IEEE J. Sel. Top. Signal Process.

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Abstract-A novel localization approach is proposed in order to find the position of an individual source using recordings of a single microphone in a reverberant enclosure. The multipath propagation is modeled by multiple virtual microphones as images of the actual single microphone and a multipath distance matrix is constructed whose components consist of the squared distances between the pairs of microphones (real or virtual) or the squared distances between the microphones and the source. The distances between the actual and virtual microphones are computed from the geometry of the enclosure. The microphonesource distances correspond to the support of the early reflections in the room impulse response associated with the source signal acquisition. The low-rank property of the Euclidean distance matrix is exploited to identify this correspondence. Source localization is achieved through optimizing the location of the source matching those measurements. The recording time of the microphone and generation of the source signal is asynchronous and estimated via the proposed procedure. Furthermore, a theoretically optimal joint localization and synchronization algorithm is derived by formulating the source localization as minimization of a quartic cost function. It is shown that the global minimum of the proposed cost function can be efficiently computed by converting it to a generalized trust region sub-problem. Numerical simulations on synthetic data and real data recordings obtained by practical tests show the effectiveness of the proposed approach.

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Single-channel indoor microphone localization

Cited by 25 publications

References 14 publications

Joint speaker localization and array calibration using expectation-maximization

Joint speaker localization and array calibration using expectation-maximization

Indoor Pedestrian Self-Positioning Based on Image Acoustic Source Impulse Using a Sensor-Rich Smartphone

Spatial Sound Localization via Multipath Euclidean Distance Matrix Recovery

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