Automatic time-frequency analysis of echolocation signals using the matched Gaussian multitaper spectrogram
(2017) Interspeech 2017 p.3048-3052- Abstract
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... (More)
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.
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- author
- Sandsten, Maria LU ; Reinhold, Isabella LU and Starkhammar, Josefin LU
- organization
- publishing date
- 2017
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- keywords
- Concentration measures, Dolphin echolocation signals, Multi-component signals, Time-frequency analysis
- host publication
- INTERSPEECH 2017
- pages
- 5 pages
- publisher
- International Speech Communication Association
- conference name
- Interspeech 2017
- conference location
- Stockholm, Sweden
- conference dates
- 2017-08-20 - 2017-08-24
- external identifiers
-
- scopus:85039147311
- DOI
- 10.21437/Interspeech.2017-119
- language
- English
- LU publication?
- yes
- id
- 06a16ee6-1059-4dcd-bb1b-71271ce7e6ea
- date added to LUP
- 2018-01-08 13:29:17
- date last changed
- 2023-12-07 09:28:53
@inproceedings{06a16ee6-1059-4dcd-bb1b-71271ce7e6ea,
abstract = {{<p>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.</p>}},
author = {{Sandsten, Maria and Reinhold, Isabella and Starkhammar, Josefin}},
booktitle = {{INTERSPEECH 2017}},
keywords = {{Concentration measures; Dolphin echolocation signals; Multi-component signals; Time-frequency analysis}},
language = {{eng}},
pages = {{3048--3052}},
publisher = {{International Speech Communication Association}},
title = {{Automatic time-frequency analysis of echolocation signals using the matched Gaussian multitaper spectrogram}},
url = {{http://dx.doi.org/10.21437/Interspeech.2017-119}},
doi = {{10.21437/Interspeech.2017-119}},
year = {{2017}},
}