Stochastic Analysis of Time-Difference and Doppler Estimates for Audio Signals
Flood, Gabrielle LU ; Heyden, Anders LU
and Åström, Kalle
LU
(2019)
7th International Conference on Pattern Recognition Applications and Methods, ICPRAM 2018
In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
11351 LNCS.
p.116-138
- 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... (More)
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.
(Less)
- author
- Flood, Gabrielle
LU
; Heyden, Anders
LU
and Åström, Kalle
LU
- organization
- publishing date
- 2019
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- keywords
- Doppler effect, Sub-sample methods, Time-difference of arrival, Uncertainty measure
- host publication
- Pattern Recognition Applications and Methods - 7th International Conference, ICPRAM 2018, Revised Selected Papers
- series title
- Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
- editor
- di Baja, Gabriella Sanniti ; Fred, Ana and De Marsico, Maria
- volume
- 11351 LNCS
- pages
- 23 pages
- publisher
- Springer
- conference name
- 7th International Conference on Pattern Recognition Applications and Methods, ICPRAM 2018
- conference location
- Funchal, Portugal
- conference dates
- 2018-01-16 - 2018-01-18
- external identifiers
-
- scopus:85060089291
- ISSN
- 1611-3349
- 0302-9743
- ISBN
- 9783030054984
- DOI
- 10.1007/978-3-030-05499-1_7
- language
- English
- LU publication?
- yes
- id
- accd2b07-96d1-4159-b3a7-79f530e45b45
- date added to LUP
- 2019-01-30 08:21:01
- date last changed
- 2024-03-19 00:03:00
@inproceedings{accd2b07-96d1-4159-b3a7-79f530e45b45,
abstract = {{<p>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.</p>}},
author = {{Flood, Gabrielle and Heyden, Anders and Åström, Kalle}},
booktitle = {{Pattern Recognition Applications and Methods - 7th International Conference, ICPRAM 2018, Revised Selected Papers}},
editor = {{di Baja, Gabriella Sanniti and Fred, Ana and De Marsico, Maria}},
isbn = {{9783030054984}},
issn = {{1611-3349}},
keywords = {{Doppler effect; Sub-sample methods; Time-difference of arrival; Uncertainty measure}},
language = {{eng}},
pages = {{116--138}},
publisher = {{Springer}},
series = {{Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}},
title = {{Stochastic Analysis of Time-Difference and Doppler Estimates for Audio Signals}},
url = {{http://dx.doi.org/10.1007/978-3-030-05499-1_7}},
doi = {{10.1007/978-3-030-05499-1_7}},
volume = {{11351 LNCS}},
year = {{2019}},
}