A compressed encoding scheme for approximate TDOA estimation

Elizabeth Vargas, James R. Hopgood, Keith Brown, Kartic Subr

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Accurate estimation of Time-Difference of Arrivals (TDOAs) is necessary to perform accurate sound source localization. The problem has traditionally been solved by using methods such as Generalized Cross-Correlation, which uses the entire signal to accurately estimate TDOAs. However, this could pose a problem in distributed sensor networks in which the amount of data that can be transmitted from each sensor to a fusion center is limited, such as in underwater scenarios or other challenging environments. Inspired by approaches from computer vision, in this paper we identify Scale-Invariant Feature Transform (SIFT) keypoints in the signal spectrogram. We perform cross-correlation on the signal using only the information available at those extracted keypoints. We test our algorithm in scenarios featuring different noise and reverberation conditions, and using different speech signals and source locations. We show that our algorithm can estimate Time-Difference of Arrivals (TDOAs) and the source location within an acceptable error range at a compression ratio of 40: 1.

Original languageEnglish
Title of host publication2018 26th European Signal Processing Conference (EUSIPCO)
PublisherIEEE
Pages346-350
Number of pages5
ISBN (Electronic)9789082797015
DOIs
Publication statusPublished - 3 Dec 2018
Event26th European Signal Processing Conference 2018 - Rome, Italy
Duration: 3 Sep 20187 Sep 2018

Publication series

NameEuropean Signal Processing Conference (EUSIPCO)
PublisherIEEE
ISSN (Electronic)2076-1465

Conference

Conference26th European Signal Processing Conference 2018
Abbreviated titleEUSIPCO 2018
CountryItaly
CityRome
Period3/09/187/09/18

Fingerprint

Encoding (symbols)
Compression ratio (machinery)
Reverberation
Acoustic noise
Computer vision
Sensor networks
Fusion reactions
Mathematical transformations
Acoustic waves
Sensors
Time difference of arrival

Keywords

  • Microphone arrays
  • Signal compressed encoding
  • Time difference estimation

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering

Cite this

Vargas, E., Hopgood, J. R., Brown, K., & Subr, K. (2018). A compressed encoding scheme for approximate TDOA estimation. In 2018 26th European Signal Processing Conference (EUSIPCO) (pp. 346-350). (European Signal Processing Conference (EUSIPCO)). IEEE. https://doi.org/10.23919/EUSIPCO.2018.8553197
Vargas, Elizabeth ; Hopgood, James R. ; Brown, Keith ; Subr, Kartic. / A compressed encoding scheme for approximate TDOA estimation. 2018 26th European Signal Processing Conference (EUSIPCO). IEEE, 2018. pp. 346-350 (European Signal Processing Conference (EUSIPCO)).
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Vargas, E, Hopgood, JR, Brown, K & Subr, K 2018, A compressed encoding scheme for approximate TDOA estimation. in 2018 26th European Signal Processing Conference (EUSIPCO). European Signal Processing Conference (EUSIPCO), IEEE, pp. 346-350, 26th European Signal Processing Conference 2018, Rome, Italy, 3/09/18. https://doi.org/10.23919/EUSIPCO.2018.8553197

A compressed encoding scheme for approximate TDOA estimation. / Vargas, Elizabeth; Hopgood, James R.; Brown, Keith; Subr, Kartic.

2018 26th European Signal Processing Conference (EUSIPCO). IEEE, 2018. p. 346-350 (European Signal Processing Conference (EUSIPCO)).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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N2 - Accurate estimation of Time-Difference of Arrivals (TDOAs) is necessary to perform accurate sound source localization. The problem has traditionally been solved by using methods such as Generalized Cross-Correlation, which uses the entire signal to accurately estimate TDOAs. However, this could pose a problem in distributed sensor networks in which the amount of data that can be transmitted from each sensor to a fusion center is limited, such as in underwater scenarios or other challenging environments. Inspired by approaches from computer vision, in this paper we identify Scale-Invariant Feature Transform (SIFT) keypoints in the signal spectrogram. We perform cross-correlation on the signal using only the information available at those extracted keypoints. We test our algorithm in scenarios featuring different noise and reverberation conditions, and using different speech signals and source locations. We show that our algorithm can estimate Time-Difference of Arrivals (TDOAs) and the source location within an acceptable error range at a compression ratio of 40: 1.

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Vargas E, Hopgood JR, Brown K, Subr K. A compressed encoding scheme for approximate TDOA estimation. In 2018 26th European Signal Processing Conference (EUSIPCO). IEEE. 2018. p. 346-350. (European Signal Processing Conference (EUSIPCO)). https://doi.org/10.23919/EUSIPCO.2018.8553197