Time Delay Estimation

Chen, Jingdong; Huang, Yiteng; Benesty, Jacob

doi:10.1007/1-4020-7769-6_8

Cited by 40 publications

(29 citation statements)

References 32 publications

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“…In either case, the normalized time delay τ jk can now be considered as the time difference of arrival (TDOA) [30], [31] of source s k between sensor j and sensor J or pair(j).…”

Section: B Propagation Model With Time Delays and Attenuationsmentioning

confidence: 99%

“…This section presents a procedure for minimizing the cost function D x in (30), and for obtaining a classification C(f, t) of observation vectors x(f, t) for the T-F masking separation described in Sec. II-B.…”

Section: Classification Of Observations For T-f Maskingmentioning

confidence: 99%

“…3) It is shown that the time delay parameters in the formulation are estimated with a function similar to the Generalized Cross Correlation PHAse Transform (GCC-PHAT) function [23], [28]- [30]. And the proposed procedure inherits the attractive properties of our recently proposed approaches [16], [22]:…”

Section: Introductionmentioning

confidence: 99%

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Grouping Separated Frequency Components by Estimating Propagation Model Parameters in Frequency-Domain Blind Source Separation

Sawada

Araki

Mukai

et al. 2007

IEEE Trans. Audio Speech Lang. Process.

109

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Abstract-This paper proposes a new formulation and optimization procedure for grouping frequency components in frequency-domain blind source separation (BSS). We adopt two separation techniques, independent component analysis (ICA) and time-frequency (T-F) masking, for the frequency-domain BSS. With ICA, grouping the frequency components corresponds to aligning the permutation ambiguity of the ICA solution in each frequency bin. With T-F masking, grouping the frequency components corresponds to classifying sensor observations in the time-frequency domain for individual sources. The grouping procedure is based on estimating anechoic propagation model parameters by analyzing ICA results or sensor observations. More specifically, the time delays of arrival and attenuations from a source to all sensors are estimated for each source. The focus of this paper includes the applicability of the proposed procedure for a situation with wide sensor spacing where spatial aliasing may occur. Experimental results show that the proposed procedure effectively separates two or three sources with several sensor configurations in a real room, as long as the room reverberation is moderately low.

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“…In either case, the normalized time delay τ jk can now be considered as the time difference of arrival (TDOA) [30], [31] of source s k between sensor j and sensor J or pair(j).…”

Section: B Propagation Model With Time Delays and Attenuationsmentioning

confidence: 99%

Section: Classification Of Observations For T-f Maskingmentioning

confidence: 99%

See 1 more Smart Citation

Grouping Separated Frequency Components by Estimating Propagation Model Parameters in Frequency-Domain Blind Source Separation

Sawada

Araki

Mukai

et al. 2007

IEEE Trans. Audio Speech Lang. Process.

109

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“…A more profound discussion follows in Section 4. The topic is also well covered in the literature; see, for example, [1,2,[13][14][15][16][17]. Figure 1 shows on the left a point-like sound source.…”

Section: Delay-based Localization Of a Sound Sourcementioning

confidence: 99%

“…The estimation of TDOAs by both methods is well documented [13,[15][16][17][19][20][21] and is not further elaborated here. As a very general statement, correlation methods are faster to compute than blind estimation methods.…”

Section: Delay-based Localization Of a Sound Sourcementioning

confidence: 99%

Acoustic Source Localization under Variable Speed of Sound Conditions

Rabenstein

Annibale

2017

Wireless Communications and Mobile Computing

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The rich literature on acoustic source localization mostly relies on the assumption of a constant value for the speed of sound. This hypothesis allows establishing simple relations between range differences and time differences and leads to effective estimation algorithms. However, it must be challenged for certain applications of wireless acoustic sensor networks in multizone buildings and outdoor environments. This article revisits the source localization problem for the more general case of an unknown value for the speed of sound. It reviews the physical foundations for the dependence of the speed of sound on the air temperature and presents the essential approaches to acoustic source localization. On this basis, several methods for source localization under uncertain or variable speed of sound conditions from the literature are discussed. Applications from different fields are shown. They comprise the localization of sources, sensors, and reflecting surfaces, time-difference-of-arrival disambiguation, and the direct determination of the speed of sound or the air temperature from acoustic measurements.

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