A practical methodology for speech source localization with microphone arrays

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

doi:10.1006/csla.1996.0024

Cited by 269 publications

(198 citation statements)

References 33 publications

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“…To avoid iterative algorithms, two-stage, closed-form LS estimators have been extensively developed for ML approximation (Friedlander, 1987;Schau and Robinson, 1987;Smith and Abel, 1987a;Smith and Abel, 1987b;Chan and Ho, 1994;Brandstein and Silverman, 1997;Huang et al, 2001;and Cheung et al, 2004). These LS solutions can provide good initialization for iterative estimators, which converge with less computational effort to a source position estimate with higher accuracy Abel, 1987a andChan et al, 2006a).…”

Section: A Toa and Tdoa-based Algorithms With Losmentioning

confidence: 99%

“…Electronically steerable arrays of microphones that apply TDOA technologies are used in a variety of ways in speech data acquisition systems (Brandstein and Silverman, 1997). …”

Section: E Other Applicationsmentioning

confidence: 99%

“…has been a subject of research for decades and continues to receive much interest in the signal processing research community, including radar (Bahl andPadmanabhan, 2000 andYan et al, 2007), sonar (Carter, 1981;Leonard and Durrant-Whyte, 1991;and Leonard and Durrant-Whyte, 2012), mobile communications (Caffery, 2000;Gustafsson and Gunnarsson, 2005;and Caffery and Stuber, 1998), multimedia (Brandstein and Silverman, 1997;Wang and Chu, 1997;and Akyildiz et al, 2007), animal tracking (Spiesberger and Fristrup, 1990), wireless sensor networks (WSNs; Akyildiz et al, 2002;Chen et al, 2002;Patwari et al, 2005;and Mao et al, 2007), and the Global Positioning System (GPS; Hofmann-Wellenhof et al, 2013). However, uncertainties resulting from the modulation of a signal propagated through an inhomogeneous medium and in measurements of received signals can quickly degrade position estimation accuracy when using standard methods to estimate transmitter position (Sayed et al, 2005;Güvenç and Chong, 2009;and Tan et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Contributed Review: Source-localization algorithms and applications using time of arrival and time difference of arrival measurements

Deng

Rauchenstein

et al. 2016

Review of Scientific Instruments

119

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Locating the position of fixed or mobile sources (i.e., transmitters) based on measurements obtained from sensors (i.e., receivers) is an important research area that is attracting much interest. In this paper, we review several representative localization algorithms that use time of arrivals (TOAs) and time difference of arrivals (TDOAs) to achieve high signal source position estimation accuracy when a transmitter is in the line-of-sight of a receiver. Circular (TOA) and hyperbolic (TDOA) position estimation approaches both use nonlinear equations that relate the known locations of receivers and unknown locations of transmitters. Estimation of the location of transmitters using the standard nonlinear equations may not be very accurate because of receiver location errors, receiver measurement errors, and computational efficiency challenges that result in high computational burdens. Least squares and maximum likelihood based algorithms have become the most popular computational approaches to transmitter location estimation. In this paper, we summarize the computational characteristics and position estimation accuracies of various positioning algorithms. By improving methods for estimating the time-of-arrival of transmissions at receivers and transmitter location estimation algorithms, transmitter location estimation may be applied across a range of applications

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Section: A Toa and Tdoa-based Algorithms With Losmentioning

confidence: 99%

“…Electronically steerable arrays of microphones that apply TDOA technologies are used in a variety of ways in speech data acquisition systems (Brandstein and Silverman, 1997). …”

Section: E Other Applicationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Contributed Review: Source-localization algorithms and applications using time of arrival and time difference of arrival measurements

Deng

Rauchenstein

et al. 2016

Review of Scientific Instruments

119

View full text Add to dashboard Cite

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“…Microphone array based speech acquisition is a common practice in hands-free interfaces in state-of-the-art applications such as distant speech recognition [1], speech separation [2], [3] and speaker localization [4], [5]. Multi-channel signal acquisition relies on beamforming or spatial filtering for directional discrimination, and space-time filtering of the signals in the acoustic scene [6], [7].…”

Section: Introductionmentioning

confidence: 99%

Microphone array beampattern characterization for hands-free speech applications

Taghizadeh

Garner

Bourlard

2012

2012 IEEE 7th Sensor Array and Multichannel Signal Processing Workshop (SAM)

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Abstract-Spatial filtering is the fundamental characteristic of microphone array based signal acquisition, which plays an important role in applications such as speech enhancement and distant speech recognition. In the array processing literature, this property is formulated upon beam-pattern steering and it is characterized for narrowband signals.This paper proposes to characterize the microphone array broadband beam-pattern based on the average output of a steered beamformer for a broadband spectrum. Relying on this characterization, we derive the directivity beam-pattern of delayand-sum and superdirective beamformers for a linear as well as a circular microphone array. We further investigate how the broadband beam-pattern is linked to speech recognition feature extraction; hence, it can be used to evaluate distant speech recognition performance. The proposed theory is demonstrated with experiments on real data recordings.Index Terms-Broadband beam-pattern, Delay-and-sum beamformer, Superdirective beamformer, Distant speech recognition.

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“…Furthermore, the RTF is assumed to be constant during the observation interval. Hence, very long observation intervals also restrict the capability of this technique to track time-varying systems (e.g., tracking moving talkers in hands-free communication scenarios [7]- [9]). Additionally, a fundamental assumption is that the interfering signals remain stationary during the entire observation interval.…”

Section: Introductionmentioning

confidence: 99%

Relative Transfer Function Identification Using Speech Signals

Cohen

2004

IEEE Trans. Speech Audio Process.

139

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Abstract-An important component of a multichannel hands-free communication system is the identification of the relative transfer function between sensors in response to a desired source signal. In this paper, a robust system identification approach adapted to speech signals is proposed. A weighted least-squares optimization criterion is introduced, which considers the uncertainty of the desired signal presence in the observed signals. An asymptotically unbiased estimate for the system's transfer function is derived, and a corresponding recursive online implementation is presented. We show that compared to a competing nonstationarity-based method, a smaller error variance is achieved and generally shorter observation intervals are required. Furthermore, in the case of a time-varying system, faster convergence and higher reliability of the system identification are obtained by using the proposed method than by using the nonstationarity-based method. Evaluation of the proposed system identification approach is performed under various noise conditions, including simulated stationary and nonstationary white Gaussian noise, and car interior noise in real pseudo-stationary and nonstationary environments. The experimental results confirm the advantages of proposed approach.

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A practical methodology for speech source localization with microphone arrays

Cited by 269 publications

References 33 publications

Contributed Review: Source-localization algorithms and applications using time of arrival and time difference of arrival measurements

Contributed Review: Source-localization algorithms and applications using time of arrival and time difference of arrival measurements

Microphone array beampattern characterization for hands-free speech applications

Relative Transfer Function Identification Using Speech Signals

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