Acoustic source location in noisy and reverberant environment using CSP analysis

Omologo, Maurizio; Svaizer, Piergiorgio

doi:10.1109/icassp.1996.543272

Cited by 103 publications

(56 citation statements)

References 9 publications

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“…The analysis window used in our experiments has duration of 60ms with an overlap of 20ms. Interpolation To increase the precision of the TDOA estimation and to be able to get better approximation for sound source location, we perform interpolation of the normalized cross-correlation before finding the maximum [7] using a windowed sinc filter. This allows us to work with sub-sample precision in TDOA estimation.…”

Section: Time Difference Of Arrival: Estimation and Modelingmentioning

confidence: 99%

Acoustic source localization in distributed sensor networks

Ajdler

Kozintsev

Lienhart

et al.

Conference Record of the Thirty-Eighth Asilomar Conference on Signals, Systems and Computers, 2004.

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This paper studies the problem of sound source localization in a distributed wireless sensor network formed by mobile general purpose computing and communication devices with audio I/O capabilities. In contrast to well understood localization methods based on dedicated microphone arrays, in our setting sound localization is performed using a sparse array of arbitrary placed sensors (in a typical scenario, localization is performed by several laptops/PDAs co-located in a room). Therefore any far-field assumptions are no longer valid in this situation. Additionally, localization algorithm's performance is affected by uncertainties in sensor position and errors in A/D synchronization. The proposed source localization algorithm consists of two steps. In the first step, time differences of arrivals (TDOAs) are estimated for the microphone pairs, and in the second step the maximum likelihood (ML) estimation for the source position is performed. We evaluate the Cramer-Rao bound (CRB) on the variance of the location estimation and compare it with simulations and experimental results. We also discuss the effects of distributed array geometry and errors in sensor positions on the performance of the localization algorithm. The performances of the system are likely to be limited by errors in sensor locations and increase when the microphones have a large aperture with respect to the source.

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Section: Time Difference Of Arrival: Estimation and Modelingmentioning

confidence: 99%

Acoustic source localization in distributed sensor networks

Ajdler

Kozintsev

Lienhart

et al.

Conference Record of the Thirty-Eighth Asilomar Conference on Signals, Systems and Computers, 2004.

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“…This section describes the estimation of the person's direction (horizontal localization) from the voice. As the home video system may require a small computation resource due to its limitations in computing capability, the CSP (Crosspower-Spectrum Phase)-based technique [8] has been implemented on the video-editing system for a real-time location system. The crosspower-spectum is computed through the short-term Fourier transform applied to windowed segments of the signal x i [t] received by the i-th microphone at time t: Then the normalized crosspower-spectrum is computed by…”

Section: : Estimation Of Sound Source Direction With Cspmentioning

confidence: 99%

Audio-Based Video Editing with Two-Channel Microphone

Takiguchi

Adachi

Ariki

2008

2008 International Conference on Multimedia and Ubiquitous Engineering (Mue 2008)

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“…The generalized cross-correlation is typically used where the peak location of the cross-correlation function of the signal of two microphones is mapped to an angular spectrum for direction of arrival estimation [1]. A weighting scheme is often employed to increase the robustness of this approach to noise and multi-path effect [2,3]. The TDOA information can also be used to design a beamformer for directional sound acquisition.…”

Section: Introductionmentioning

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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Acoustic source location in noisy and reverberant environment using CSP analysis

Cited by 103 publications

References 9 publications

Acoustic source localization in distributed sensor networks

Acoustic source localization in distributed sensor networks

Audio-Based Video Editing with Two-Channel Microphone

Spatial Sound Localization via Multipath Euclidean Distance Matrix Recovery

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