Automatic time-frequency analysis of echolocation signals using the matched Gaussian multitaper spectrogram
High-resolution time-frequency (TF) images of multi-component signals are of great interest for visualization, feature extraction and estimation. The matched Gaussian multitaper spectrogram has been proposed to optimally resolve multi-component transient functions of Gaussian shape. Hermite function...
Published in: | Interspeech 2017 |
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International Speech Communication Association
2017
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Online Access: | https://lup.lub.lu.se/record/06a16ee6-1059-4dcd-bb1b-71271ce7e6ea https://doi.org/10.21437/Interspeech.2017-119 |
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ftulundlup:oai:lup.lub.lu.se:06a16ee6-1059-4dcd-bb1b-71271ce7e6ea 2024-01-07T09:42:26+01:00 Automatic time-frequency analysis of echolocation signals using the matched Gaussian multitaper spectrogram Sandsten, Maria Reinhold, Isabella Starkhammar, Josefin 2017 https://lup.lub.lu.se/record/06a16ee6-1059-4dcd-bb1b-71271ce7e6ea https://doi.org/10.21437/Interspeech.2017-119 eng eng International Speech Communication Association https://lup.lub.lu.se/record/06a16ee6-1059-4dcd-bb1b-71271ce7e6ea http://dx.doi.org/10.21437/Interspeech.2017-119 scopus:85039147311 Signal Processing Concentration measures Dolphin echolocation signals Multi-component signals Time-frequency analysis contributiontobookanthology/conference info:eu-repo/semantics/conferencePaper text 2017 ftulundlup https://doi.org/10.21437/Interspeech.2017-119 2023-12-13T23:29:10Z High-resolution time-frequency (TF) images of multi-component signals are of great interest for visualization, feature extraction and estimation. The matched Gaussian multitaper spectrogram has been proposed to optimally resolve multi-component transient functions of Gaussian shape. Hermite functions are used as multitapers and the weights of the different spectrogram functions are optimized. For a fixed number of multitapers, the optimization gives the approximate Wigner distribution of the Gaussian shaped function. Increasing the number of multitapers gives a better approximation, i.e. a better resolution, but the cross-terms also become more prominent for close TF components. In this submission, we evaluate a number of different concentration measures to automatically estimate the number of multitapers resulting in the optimal spectrogram for TF images of dolphin echolocation signals. The measures are evaluated for different multi-component signals and noise levels and a suggestion of an automatic procedure for optimal TF analysis is given. The results are compared to other well known TF estimation algorithms and examples of real data measurements of echolocation signals from a beluga whale (Delphinapterus leucas) are presented. Book Part Beluga Beluga whale Beluga* Delphinapterus leucas Lund University Publications (LUP) Interspeech 2017 3048 3052 |
institution |
Open Polar |
collection |
Lund University Publications (LUP) |
op_collection_id |
ftulundlup |
language |
English |
topic |
Signal Processing Concentration measures Dolphin echolocation signals Multi-component signals Time-frequency analysis |
spellingShingle |
Signal Processing Concentration measures Dolphin echolocation signals Multi-component signals Time-frequency analysis Sandsten, Maria Reinhold, Isabella Starkhammar, Josefin Automatic time-frequency analysis of echolocation signals using the matched Gaussian multitaper spectrogram |
topic_facet |
Signal Processing Concentration measures Dolphin echolocation signals Multi-component signals Time-frequency analysis |
description |
High-resolution time-frequency (TF) images of multi-component signals are of great interest for visualization, feature extraction and estimation. The matched Gaussian multitaper spectrogram has been proposed to optimally resolve multi-component transient functions of Gaussian shape. Hermite functions are used as multitapers and the weights of the different spectrogram functions are optimized. For a fixed number of multitapers, the optimization gives the approximate Wigner distribution of the Gaussian shaped function. Increasing the number of multitapers gives a better approximation, i.e. a better resolution, but the cross-terms also become more prominent for close TF components. In this submission, we evaluate a number of different concentration measures to automatically estimate the number of multitapers resulting in the optimal spectrogram for TF images of dolphin echolocation signals. The measures are evaluated for different multi-component signals and noise levels and a suggestion of an automatic procedure for optimal TF analysis is given. The results are compared to other well known TF estimation algorithms and examples of real data measurements of echolocation signals from a beluga whale (Delphinapterus leucas) are presented. |
format |
Book Part |
author |
Sandsten, Maria Reinhold, Isabella Starkhammar, Josefin |
author_facet |
Sandsten, Maria Reinhold, Isabella Starkhammar, Josefin |
author_sort |
Sandsten, Maria |
title |
Automatic time-frequency analysis of echolocation signals using the matched Gaussian multitaper spectrogram |
title_short |
Automatic time-frequency analysis of echolocation signals using the matched Gaussian multitaper spectrogram |
title_full |
Automatic time-frequency analysis of echolocation signals using the matched Gaussian multitaper spectrogram |
title_fullStr |
Automatic time-frequency analysis of echolocation signals using the matched Gaussian multitaper spectrogram |
title_full_unstemmed |
Automatic time-frequency analysis of echolocation signals using the matched Gaussian multitaper spectrogram |
title_sort |
automatic time-frequency analysis of echolocation signals using the matched gaussian multitaper spectrogram |
publisher |
International Speech Communication Association |
publishDate |
2017 |
url |
https://lup.lub.lu.se/record/06a16ee6-1059-4dcd-bb1b-71271ce7e6ea https://doi.org/10.21437/Interspeech.2017-119 |
genre |
Beluga Beluga whale Beluga* Delphinapterus leucas |
genre_facet |
Beluga Beluga whale Beluga* Delphinapterus leucas |
op_relation |
https://lup.lub.lu.se/record/06a16ee6-1059-4dcd-bb1b-71271ce7e6ea http://dx.doi.org/10.21437/Interspeech.2017-119 scopus:85039147311 |
op_doi |
https://doi.org/10.21437/Interspeech.2017-119 |
container_title |
Interspeech 2017 |
container_start_page |
3048 |
op_container_end_page |
3052 |
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1787423411244367872 |