The mutual information function and its application to signal processing / von Hans-Peter Bernhard
eng: In this thesis, we introduce a new signal analysis method: the mutual information function. It is designed to deal with output signals of nonlinear systems, unlike the correlation function which reflects only linear system properties. We use the mutual information function to analyze speech si...
Saved in:
VerfasserIn: | |
---|---|
Place / Publishing House: | 1997 |
Year of Publication: | 1997 |
Language: | English |
Subjects: | |
Physical Description: | 104 Bl.; Ill., graph. Darst. |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
id |
990000957230504498 |
---|---|
ctrlnum |
AC02216641 (AT-OBV)AC02216641 (Aleph)002162074ACC01 (DE-599)OBVAC02216641 (EXLNZ-43ACC_NETWORK)990021620740203331 |
collection |
bib_alma |
record_format |
marc |
spelling |
Bernhard, Hans-Peter aut <<The>> mutual information function and its application to signal processing von Hans-Peter Bernhard 1997 104 Bl. Ill., graph. Darst. Wien, Techn. Univ., Diss., 1997 eng: In this thesis, we introduce a new signal analysis method: the mutual information function. It is designed to deal with output signals of nonlinear systems, unlike the correlation function which reflects only linear system properties. We use the mutual information function to analyze speech signals with the aim of developing a new modeling concept for voiced speech based on nonlinear systems theory. With this method, we determine the dimensionality and dynamic behavior of a low- (4- to 5-) dimensional, deterministic nonlinear oscillator as a new model concept that improves on the prevalent linear source-filter model. A theorem is derived that provides a tight upper bound on the prediction gain of linear and nonlinear predictors for stationary stochastic processes. This very general approach is applied to different system types and their output processes. We illustrate how the mutual information function can be used to compute this bound for several examples. In an energy management systems application, we show the capability of the mutual information function to estimate the maximum prediction gain for load curve forecasting. By input variable selection we could show that just 5 input variables are sufficient to achieve a prediction gain which lies above the gain of linear forecasting systems which rely on more than 100 input variables. As the signal processing core of our approach, we have developed a fast algorithm for the estimation of the mutual information function for a given single realization of a stationary process. Zsfassung in dt. Sprache Signalverarbeitung s (DE-588)4054947-1 Information s (DE-588)4026899-8 AT-OBV UBL |
language |
English |
format |
Thesis Book |
author |
Bernhard, Hans-Peter |
spellingShingle |
Bernhard, Hans-Peter The mutual information function and its application to signal processing Signalverarbeitung (DE-588)4054947-1 Information (DE-588)4026899-8 |
author_facet |
Bernhard, Hans-Peter |
author_variant |
h p b hpb |
author_role |
VerfasserIn |
author_sort |
Bernhard, Hans-Peter |
title |
The mutual information function and its application to signal processing |
title_full |
The mutual information function and its application to signal processing von Hans-Peter Bernhard |
title_fullStr |
The mutual information function and its application to signal processing von Hans-Peter Bernhard |
title_full_unstemmed |
The mutual information function and its application to signal processing von Hans-Peter Bernhard |
title_auth |
The mutual information function and its application to signal processing |
title_new |
The mutual information function and its application to signal processing |
title_sort |
mutual information function and its application to signal processing |
publishDate |
1997 |
physical |
104 Bl. Ill., graph. Darst. |
topic |
Signalverarbeitung (DE-588)4054947-1 Information (DE-588)4026899-8 |
topic_facet |
Signalverarbeitung Information |
illustrated |
Illustrated |
work_keys_str_mv |
AT bernhardhanspeter themutualinformationfunctionanditsapplicationtosignalprocessing |
status_str |
n |
ids_txt_mv |
(AT-OBV)AC02216641 AC02216641 (Aleph)002162074ACC01 (DE-599)OBVAC02216641 (EXLNZ-43ACC_NETWORK)990021620740203331 |
is_hierarchy_id |
AC02216641 |
is_hierarchy_title |
<<The>> mutual information function and its application to signal processing |
_version_ |
1798471846146867201 |
fullrecord |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>02485nam#a2200325zc#4500</leader><controlfield tag="001">990000957230504498</controlfield><controlfield tag="005">20231120135746.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">980130|1997####|||######m####|||#|#eng#c</controlfield><controlfield tag="009">AC02216641</controlfield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(AT-OBV)AC02216641</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">AC02216641</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(Aleph)002162074ACC01</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)OBVAC02216641</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(EXLNZ-43ACC_NETWORK)990021620740203331</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">UBL</subfield><subfield code="b">ger</subfield><subfield code="d">UBL</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="044" ind1=" " ind2=" "><subfield code="c">XA-AT</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Bernhard, Hans-Peter</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">The mutual information function and its application to signal processing</subfield><subfield code="c">von Hans-Peter Bernhard</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">1997</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">104 Bl.</subfield><subfield code="b">Ill., graph. Darst.</subfield></datafield><datafield tag="502" ind1=" " ind2=" "><subfield code="a">Wien, Techn. Univ., Diss., 1997</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">eng: In this thesis, we introduce a new signal analysis method: the mutual information function. It is designed to deal with output signals of nonlinear systems, unlike the correlation function which reflects only linear system properties. We use the mutual information function to analyze speech signals with the aim of developing a new modeling concept for voiced speech based on nonlinear systems theory. With this method, we determine the dimensionality and dynamic behavior of a low- (4- to 5-) dimensional, deterministic nonlinear oscillator as a new model concept that improves on the prevalent linear source-filter model. A theorem is derived that provides a tight upper bound on the prediction gain of linear and nonlinear predictors for stationary stochastic processes. This very general approach is applied to different system types and their output processes. We illustrate how the mutual information function can be used to compute this bound for several examples. In an energy management systems application, we show the capability of the mutual information function to estimate the maximum prediction gain for load curve forecasting. By input variable selection we could show that just 5 input variables are sufficient to achieve a prediction gain which lies above the gain of linear forecasting systems which rely on more than 100 input variables. As the signal processing core of our approach, we have developed a fast algorithm for the estimation of the mutual information function for a given single realization of a stationary process. </subfield></datafield><datafield tag="546" ind1=" " ind2=" "><subfield code="a">Zsfassung in dt. Sprache</subfield></datafield><datafield tag="689" ind1="0" ind2="0"><subfield code="a">Signalverarbeitung</subfield><subfield code="D">s</subfield><subfield code="0">(DE-588)4054947-1</subfield></datafield><datafield tag="689" ind1="0" ind2="1"><subfield code="a">Information</subfield><subfield code="D">s</subfield><subfield code="0">(DE-588)4026899-8</subfield></datafield><datafield tag="689" ind1="0" ind2=" "><subfield code="5">AT-OBV</subfield><subfield code="5">UBL</subfield></datafield><datafield tag="970" ind1="1" ind2=" "><subfield code="c">39</subfield></datafield><datafield tag="970" ind1="2" ind2=" "><subfield code="d">HS-DISS</subfield></datafield><datafield tag="974" ind1="0" ind2="u"><subfield code="V">903</subfield><subfield code="a">21</subfield></datafield><datafield tag="ADM" ind1=" " ind2=" "><subfield code="b">2024-05-08 08:53:44 Europe/Vienna</subfield><subfield code="d">20</subfield><subfield code="f">System</subfield><subfield code="c">marc21</subfield><subfield code="a">2018-12-24 09:47:13 Europe/Vienna</subfield><subfield code="g">false</subfield></datafield></record></collection> |