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 language | English |
|---|---|
| Title of host publication | 2018 26th European Signal Processing Conference (EUSIPCO) |
| Publisher | IEEE |
| Pages | 346-350 |
| Number of pages | 5 |
| ISBN (Electronic) | 9789082797015 |
| DOIs | |
| Publication status | Published - 3 Dec 2018 |
| Event | 26th European Signal Processing Conference 2018 - Rome, Italy Duration: 3 Sep 2018 → 7 Sep 2018 |
Publication series
| Name | European Signal Processing Conference (EUSIPCO) |
|---|---|
| Publisher | IEEE |
| ISSN (Electronic) | 2076-1465 |
Conference
| Conference | 26th European Signal Processing Conference 2018 |
|---|---|
| Abbreviated title | EUSIPCO 2018 |
| Country | Italy |
| City | Rome |
| Period | 3/09/18 → 7/09/18 |
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Keywords
- Microphone arrays
- Signal compressed encoding
- Time difference estimation
ASJC Scopus subject areas
- Signal Processing
- Electrical and Electronic Engineering
Cite this
}
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 proceeding › Conference contribution
TY - GEN
T1 - A compressed encoding scheme for approximate TDOA estimation
AU - Vargas, Elizabeth
AU - Hopgood, James R.
AU - Brown, Keith
AU - Subr, Kartic
PY - 2018/12/3
Y1 - 2018/12/3
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.
AB - 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.
KW - Microphone arrays
KW - Signal compressed encoding
KW - Time difference estimation
UR - http://www.scopus.com/inward/record.url?scp=85059802220&partnerID=8YFLogxK
U2 - 10.23919/EUSIPCO.2018.8553197
DO - 10.23919/EUSIPCO.2018.8553197
M3 - Conference contribution
T3 - European Signal Processing Conference (EUSIPCO)
SP - 346
EP - 350
BT - 2018 26th European Signal Processing Conference (EUSIPCO)
PB - IEEE
ER -