A practical time-delay estimator for localizing speech sources with a microphone array

Brandstein, M.S.; Adcock, John; Silverman, H.F.

doi:10.1006/csla.1995.0009

Cited by 97 publications

(64 citation statements)

References 15 publications

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“…Expected There have been extensive theoretical studies on sound-based localization using both pairs of microphones and microphone arrays [ 9,10,11,12,13]. However, there has been much less work on implementing and experimenting soundbased localization realized as customized electronic designs.…”

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confidence: 99%

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Improved sound-based localization through a network of reconfigurable mixed-signal nodes

Umbarkar

Subramanian

Doboli

2010

2010 IEEE International Workshop on Robotic and Sensors Environments

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confidence: 99%

“…A sliding window mechanism is used to continuously sample and process in- Localization using microphone arrays is discussed in [12]. Ledeczi et al [1] discuss a sensor network implementation for sound-based detection of countersniper position.…”

Section: Sn1 Sn2 Sn3 Sn4mentioning

confidence: 99%

Improved sound-based localization through a network of reconfigurable mixed-signal nodes

Umbarkar

Subramanian

Doboli

2010

2010 IEEE International Workshop on Robotic and Sensors Environments

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“…The TDE method using the inter-channel phase difference (IPD) has been attracted a lot since 1980s, thanks to its advantage on obtaining the result instantaneously [19][20][21][22][23]. Chan et al [19] verified that a least-square (LS) estimator to the phase slope of cross power spectrum was equivalent to the ML estimator.…”

Section: Introductionmentioning

confidence: 99%

Two-stage source tracking method using a multiple linear regression model in the expanded phase domain

Yang

Kang

2012

EURASIP J. Adv. Signal Process.

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This article proposes an efficient two-channel time delay estimation method for tracking a moving speaker in noisy and re-verberant environment. Unlike conventional linear regression model-based methods, the proposed multiple linear regression model designed in the expanded phase domain shows high estimation accuracy in adverse condition because its the Gaussian assumption on phase distribution is valid. Therefore, the least-square-based time delay estimator using the proposed multiple linear regression model becomes an ideal estimator that does not require a complicated phase unwrapping process. In addition, the proposed method is extended to the twostage recursive estimation approach, which can be used for a moving source tracking scenario. The performance of the proposed method is compared with that of conventional cross-correlation and linear regression-based methods in noisy and reverberant environment. Experimental results verify that the proposed algorithm significantly decreases estimation anomalies and improves the accuracy of time delay estimation. Finally, the tracking performance of the proposed method to both slow and fast moving speakers is confirmed in adverse environment.

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“…In particular, several analytical frameworks have been proposed for the estimation of the time difference of arrival (TDOA). Most of the methods are based on measuring the crosscorrelation (in time or frequency domains) between the output at different receivers [8,9]. An interesting approach is that proposed in [10], where the authors derive a unified ML framework for sound source localization and beamforming for distributed meeting applications, taking into account both reverberation and environmental noise.…”

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confidence: 99%

Low-Complexity Audio Signal Processing for Localization in Indoor Scenarios

Martalò

Ferrari

2010

The Internet of Things

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During the last years, wireless sensor networks (WSNs) have received significant attention from the research community, as one of the emerging technologies for the new millennium. A WSN is composed by many (e.g., hundreds) devices with limited processing and communication capabilities. Therefore, energy saving is one of the major issues and information processing has to be performed with low complexity. Possible applications for WSNs are surveillance, environmental monitoring, flow control, etc, and it may be possible to work in indoor scenarios [1]. The application of interest in this paper is the localization of a person or an object in indoor scenarios [2], but our approach is also suitable for outdoor scenarios. We will refer to the person (or object) to be localized as the entity. In these scenarios, it is realistic to assume that the nodes are not equipped with global positioning system (GPS) devices and, therefore, other techniques are needed to perform efficient localization. Most of the works about localization in sensor networks are based on the assumption that the entities are equipped with devices which radio-communicate with some reference nodes (denoted as anchors). The positions of the anchors are supposed to be known and the position of the entity of interest is inferred by “combining” the information available at each anchor (e.g., by triangulation). In the literature, several techniques, based on different methods, have been proposed to obtain a sufficiently low estimation error [3, 4]. The computational complexity of these algorithms is a crucial issue for WSN-based applications. In [5], the authors propose a sub-optimal hierarchical algorithm, which solves the localization problem without resorting to the optimum maximum likelihood (ML) technique, whose computational complexity becomes too high to be of any practical interest. In [6], an adaptive approach to localization problems, obtained by solving a sequence of very small optimization subproblems, is considered

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A practical time-delay estimator for localizing speech sources with a microphone array

Cited by 97 publications

References 15 publications

Improved sound-based localization through a network of reconfigurable mixed-signal nodes

Improved sound-based localization through a network of reconfigurable mixed-signal nodes

Two-stage source tracking method using a multiple linear regression model in the expanded phase domain

Low-Complexity Audio Signal Processing for Localization in Indoor Scenarios

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