Stochastic Analysis of Time-Difference and Doppler Estimates for Audio Signals

Gabrielle Flood; Anders Heyden; Kalle Åström

doi:10.1007/978-3-030-05499-1_7

Stochastic Analysis of Time-Difference and Doppler Estimates for Audio Signals

Gabrielle Flood, Anders Heyden, Kalle Åström

Research output: Chapter in Book/Report/Conference proceeding › Paper in conference proceeding › peer-review

Abstract

Pairwise comparison of sound and radio signals can be used to estimate the distance between two units that send and receive signals. In a similar way it is possible to estimate differences of distances by correlating two received signals. There are essentially two groups of such methods, namely methods that are robust to noise and reverberation, but give limited precision and sub-sample refinements that are more sensitive to noise, but also give higher precision when they are initialized close to the real translation. In this paper, we present stochastic models that can explain the precision limits of such sub-sample time-difference estimates. Using these models new methods are provided for precise estimates of time-differences as well as Doppler effects. The developed methods are evaluated and verified on both synthetic and real data.

Original language	English
Title of host publication	Pattern Recognition Applications and Methods - 7th International Conference, ICPRAM 2018, Revised Selected Papers
Editors	Gabriella Sanniti di Baja, Ana Fred, Maria De Marsico
Publisher	Springer
Pages	116-138
Number of pages	23
ISBN (Print)	9783030054984
DOIs	https://doi.org/10.1007/978-3-030-05499-1_7
Publication status	Published - 2019
Event	7th International Conference on Pattern Recognition Applications and Methods, ICPRAM 2018 - Madeira, Funchal, Portugal Duration: 2018 Jan 16 → 2018 Jan 18

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11351 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	7th International Conference on Pattern Recognition Applications and Methods, ICPRAM 2018
Abbreviated title	ICPRAM 2018
Country/Territory	Portugal
City	Funchal
Period	2018/01/16 → 2018/01/18

Subject classification (UKÄ)

Mathematical Sciences

Free keywords

Doppler effect
Sub-sample methods
Time-difference of arrival
Uncertainty measure

Access to Document

10.1007/978-3-030-05499-1_7

Cite this

Flood, G., Heyden, A., & Åström, K. (2019). Stochastic Analysis of Time-Difference and Doppler Estimates for Audio Signals. In G. S. di Baja, A. Fred, & M. De Marsico (Eds.), Pattern Recognition Applications and Methods - 7th International Conference, ICPRAM 2018, Revised Selected Papers (pp. 116-138). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11351 LNCS). Springer. https://doi.org/10.1007/978-3-030-05499-1_7

Flood, Gabrielle ; Heyden, Anders ; Åström, Kalle. / Stochastic Analysis of Time-Difference and Doppler Estimates for Audio Signals. Pattern Recognition Applications and Methods - 7th International Conference, ICPRAM 2018, Revised Selected Papers. editor / Gabriella Sanniti di Baja ; Ana Fred ; Maria De Marsico. Springer, 2019. pp. 116-138 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "Pairwise comparison of sound and radio signals can be used to estimate the distance between two units that send and receive signals. In a similar way it is possible to estimate differences of distances by correlating two received signals. There are essentially two groups of such methods, namely methods that are robust to noise and reverberation, but give limited precision and sub-sample refinements that are more sensitive to noise, but also give higher precision when they are initialized close to the real translation. In this paper, we present stochastic models that can explain the precision limits of such sub-sample time-difference estimates. Using these models new methods are provided for precise estimates of time-differences as well as Doppler effects. The developed methods are evaluated and verified on both synthetic and real data.",

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Flood, G , Heyden, A & Åström, K 2019, Stochastic Analysis of Time-Difference and Doppler Estimates for Audio Signals. in GS di Baja, A Fred & M De Marsico (eds), Pattern Recognition Applications and Methods - 7th International Conference, ICPRAM 2018, Revised Selected Papers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11351 LNCS, Springer, pp. 116-138, 7th International Conference on Pattern Recognition Applications and Methods, ICPRAM 2018, Funchal, Portugal, 2018/01/16. https://doi.org/10.1007/978-3-030-05499-1_7

Stochastic Analysis of Time-Difference and Doppler Estimates for Audio Signals. / Flood, Gabrielle ; Heyden, Anders ; Åström, Kalle.
Pattern Recognition Applications and Methods - 7th International Conference, ICPRAM 2018, Revised Selected Papers. ed. / Gabriella Sanniti di Baja; Ana Fred; Maria De Marsico. Springer, 2019. p. 116-138 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11351 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Paper in conference proceeding › peer-review

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T1 - Stochastic Analysis of Time-Difference and Doppler Estimates for Audio Signals

AU - Flood, Gabrielle

AU - Heyden, Anders

AU - Åström, Kalle

PY - 2019

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N2 - Pairwise comparison of sound and radio signals can be used to estimate the distance between two units that send and receive signals. In a similar way it is possible to estimate differences of distances by correlating two received signals. There are essentially two groups of such methods, namely methods that are robust to noise and reverberation, but give limited precision and sub-sample refinements that are more sensitive to noise, but also give higher precision when they are initialized close to the real translation. In this paper, we present stochastic models that can explain the precision limits of such sub-sample time-difference estimates. Using these models new methods are provided for precise estimates of time-differences as well as Doppler effects. The developed methods are evaluated and verified on both synthetic and real data.

AB - Pairwise comparison of sound and radio signals can be used to estimate the distance between two units that send and receive signals. In a similar way it is possible to estimate differences of distances by correlating two received signals. There are essentially two groups of such methods, namely methods that are robust to noise and reverberation, but give limited precision and sub-sample refinements that are more sensitive to noise, but also give higher precision when they are initialized close to the real translation. In this paper, we present stochastic models that can explain the precision limits of such sub-sample time-difference estimates. Using these models new methods are provided for precise estimates of time-differences as well as Doppler effects. The developed methods are evaluated and verified on both synthetic and real data.

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KW - Sub-sample methods

KW - Time-difference of arrival

KW - Uncertainty measure

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T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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PB - Springer

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Flood G , Heyden A , Åström K. Stochastic Analysis of Time-Difference and Doppler Estimates for Audio Signals. In di Baja GS, Fred A, De Marsico M, editors, Pattern Recognition Applications and Methods - 7th International Conference, ICPRAM 2018, Revised Selected Papers. Springer. 2019. p. 116-138. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-05499-1_7

Stochastic Analysis of Time-Difference and Doppler Estimates for Audio Signals

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Subject classification (UKÄ)

Free keywords

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Mapping and Merging Using Sound and Vision: Automatic Calibration and Map Fusion with Statistical Deformations

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