Combining statistical models using modified spectral subtraction method for embedded system

Speech enhancement aims at improving the overall speech signal perceptual quality using different audio signal processing techniques. Filtering techniques, spectral restoration and model-based methods are the three fundamental classes of speech enhancement algorithms for noise reduction. With mobile...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Microprocessors and microsystems 2020-03, Vol.73, p.102957, Article 102957
Hauptverfasser: Balaji, V.R., S, Maheswaran, Rajesh Babu, M., Kowsigan, M., E., Prabhu, K, Venkatachalam
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page
container_issue
container_start_page 102957
container_title Microprocessors and microsystems
container_volume 73
creator Balaji, V.R.
S, Maheswaran
Rajesh Babu, M.
Kowsigan, M.
E., Prabhu
K, Venkatachalam
description Speech enhancement aims at improving the overall speech signal perceptual quality using different audio signal processing techniques. Filtering techniques, spectral restoration and model-based methods are the three fundamental classes of speech enhancement algorithms for noise reduction. With mobile devices increased usage, the demand for algorithms processing also increases. As the microphones that capture the voice of the speaker are at considerable distance in most of the devices, noise are getting added thereby degrading the quality of the speech signal. No single theorist's stone or minimization standard has been discovered so far for speech enhancement. In this work, we have combined the statistical models along with machine learning algorithm to improve the results thereby resulting in robust communication. Experimentation results on AURORA 2 database shows us improved results over the state of the art methods discussed in the literature.
doi_str_mv 10.1016/j.micpro.2019.102957
format Article
fullrecord <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_2377330862</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><els_id>S0141933119304600</els_id><sourcerecordid>2377330862</sourcerecordid><originalsourceid>FETCH-LOGICAL-c400t-f10d7f2fe839858f394d5b763eb2d7ba6a14693cd7a9df56d6fccd1fc9addf1e3</originalsourceid><addsrcrecordid>eNp9kEtLxDAQx4MouK5-Aw8Fz13zaJPmIsjiCxa86MlDaJOJpmybNUmF_fam1LOnYeb_GPghdE3whmDCb_vN4PQh-A3FROYTlbU4QSvSCFrKivFTtMKkIqVkjJyjixh7jHGNOV2hj60fOje68bOIqU0uJqfbfTF4A_tYTHEW8uKsA1PEA-gUshynLk-dnB-LAdKXN4X1oYChA2Nm4zEmGC7RmW33Ea7-5hq9Pz68bZ_L3evTy_Z-V-oK41Rago2w1ELDZFM3lsnK1J3gDDpqRNfyllRcMm1EK42tueFWa0Oslq0xlgBbo5ulNyP4niAm1fspjPmlokwIxnDDaXZVi0sHH2MAqw7BDW04KoLVjFH1asGoZoxqwZhjd0ss84AfB0FF7WDUYFzINJTx7v-CX4qcgBE</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2377330862</pqid></control><display><type>article</type><title>Combining statistical models using modified spectral subtraction method for embedded system</title><source>Access via ScienceDirect (Elsevier)</source><creator>Balaji, V.R. ; S, Maheswaran ; Rajesh Babu, M. ; Kowsigan, M. ; E., Prabhu ; K, Venkatachalam</creator><creatorcontrib>Balaji, V.R. ; S, Maheswaran ; Rajesh Babu, M. ; Kowsigan, M. ; E., Prabhu ; K, Venkatachalam</creatorcontrib><description>Speech enhancement aims at improving the overall speech signal perceptual quality using different audio signal processing techniques. Filtering techniques, spectral restoration and model-based methods are the three fundamental classes of speech enhancement algorithms for noise reduction. With mobile devices increased usage, the demand for algorithms processing also increases. As the microphones that capture the voice of the speaker are at considerable distance in most of the devices, noise are getting added thereby degrading the quality of the speech signal. No single theorist's stone or minimization standard has been discovered so far for speech enhancement. In this work, we have combined the statistical models along with machine learning algorithm to improve the results thereby resulting in robust communication. Experimentation results on AURORA 2 database shows us improved results over the state of the art methods discussed in the literature.</description><identifier>ISSN: 0141-9331</identifier><identifier>EISSN: 1872-9436</identifier><identifier>DOI: 10.1016/j.micpro.2019.102957</identifier><language>eng</language><publisher>Kidlington: Elsevier B.V</publisher><subject>Algorithms ; Electronic devices ; Embedded systems ; Experimentation ; Linear models ; Machine learning ; Microphones ; Noise reduction ; Precision and recall ; Restoration ; Signal processing ; Signal quality ; Speech enhancement ; Speech processing ; Statistical models ; Subtraction ; Support vector machines ; Voice communication</subject><ispartof>Microprocessors and microsystems, 2020-03, Vol.73, p.102957, Article 102957</ispartof><rights>2019</rights><rights>Copyright Elsevier BV Mar 2020</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c400t-f10d7f2fe839858f394d5b763eb2d7ba6a14693cd7a9df56d6fccd1fc9addf1e3</citedby><cites>FETCH-LOGICAL-c400t-f10d7f2fe839858f394d5b763eb2d7ba6a14693cd7a9df56d6fccd1fc9addf1e3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/j.micpro.2019.102957$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,780,784,3550,27924,27925,45995</link.rule.ids></links><search><creatorcontrib>Balaji, V.R.</creatorcontrib><creatorcontrib>S, Maheswaran</creatorcontrib><creatorcontrib>Rajesh Babu, M.</creatorcontrib><creatorcontrib>Kowsigan, M.</creatorcontrib><creatorcontrib>E., Prabhu</creatorcontrib><creatorcontrib>K, Venkatachalam</creatorcontrib><title>Combining statistical models using modified spectral subtraction method for embedded system</title><title>Microprocessors and microsystems</title><description>Speech enhancement aims at improving the overall speech signal perceptual quality using different audio signal processing techniques. Filtering techniques, spectral restoration and model-based methods are the three fundamental classes of speech enhancement algorithms for noise reduction. With mobile devices increased usage, the demand for algorithms processing also increases. As the microphones that capture the voice of the speaker are at considerable distance in most of the devices, noise are getting added thereby degrading the quality of the speech signal. No single theorist's stone or minimization standard has been discovered so far for speech enhancement. In this work, we have combined the statistical models along with machine learning algorithm to improve the results thereby resulting in robust communication. Experimentation results on AURORA 2 database shows us improved results over the state of the art methods discussed in the literature.</description><subject>Algorithms</subject><subject>Electronic devices</subject><subject>Embedded systems</subject><subject>Experimentation</subject><subject>Linear models</subject><subject>Machine learning</subject><subject>Microphones</subject><subject>Noise reduction</subject><subject>Precision and recall</subject><subject>Restoration</subject><subject>Signal processing</subject><subject>Signal quality</subject><subject>Speech enhancement</subject><subject>Speech processing</subject><subject>Statistical models</subject><subject>Subtraction</subject><subject>Support vector machines</subject><subject>Voice communication</subject><issn>0141-9331</issn><issn>1872-9436</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><recordid>eNp9kEtLxDAQx4MouK5-Aw8Fz13zaJPmIsjiCxa86MlDaJOJpmybNUmF_fam1LOnYeb_GPghdE3whmDCb_vN4PQh-A3FROYTlbU4QSvSCFrKivFTtMKkIqVkjJyjixh7jHGNOV2hj60fOje68bOIqU0uJqfbfTF4A_tYTHEW8uKsA1PEA-gUshynLk-dnB-LAdKXN4X1oYChA2Nm4zEmGC7RmW33Ea7-5hq9Pz68bZ_L3evTy_Z-V-oK41Rago2w1ELDZFM3lsnK1J3gDDpqRNfyllRcMm1EK42tueFWa0Oslq0xlgBbo5ulNyP4niAm1fspjPmlokwIxnDDaXZVi0sHH2MAqw7BDW04KoLVjFH1asGoZoxqwZhjd0ss84AfB0FF7WDUYFzINJTx7v-CX4qcgBE</recordid><startdate>202003</startdate><enddate>202003</enddate><creator>Balaji, V.R.</creator><creator>S, Maheswaran</creator><creator>Rajesh Babu, M.</creator><creator>Kowsigan, M.</creator><creator>E., Prabhu</creator><creator>K, Venkatachalam</creator><general>Elsevier B.V</general><general>Elsevier BV</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7SP</scope><scope>8FD</scope><scope>F28</scope><scope>FR3</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope></search><sort><creationdate>202003</creationdate><title>Combining statistical models using modified spectral subtraction method for embedded system</title><author>Balaji, V.R. ; S, Maheswaran ; Rajesh Babu, M. ; Kowsigan, M. ; E., Prabhu ; K, Venkatachalam</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c400t-f10d7f2fe839858f394d5b763eb2d7ba6a14693cd7a9df56d6fccd1fc9addf1e3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Algorithms</topic><topic>Electronic devices</topic><topic>Embedded systems</topic><topic>Experimentation</topic><topic>Linear models</topic><topic>Machine learning</topic><topic>Microphones</topic><topic>Noise reduction</topic><topic>Precision and recall</topic><topic>Restoration</topic><topic>Signal processing</topic><topic>Signal quality</topic><topic>Speech enhancement</topic><topic>Speech processing</topic><topic>Statistical models</topic><topic>Subtraction</topic><topic>Support vector machines</topic><topic>Voice communication</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Balaji, V.R.</creatorcontrib><creatorcontrib>S, Maheswaran</creatorcontrib><creatorcontrib>Rajesh Babu, M.</creatorcontrib><creatorcontrib>Kowsigan, M.</creatorcontrib><creatorcontrib>E., Prabhu</creatorcontrib><creatorcontrib>K, Venkatachalam</creatorcontrib><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Electronics &amp; Communications Abstracts</collection><collection>Technology Research Database</collection><collection>ANTE: Abstracts in New Technology &amp; Engineering</collection><collection>Engineering Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts – Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><jtitle>Microprocessors and microsystems</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Balaji, V.R.</au><au>S, Maheswaran</au><au>Rajesh Babu, M.</au><au>Kowsigan, M.</au><au>E., Prabhu</au><au>K, Venkatachalam</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Combining statistical models using modified spectral subtraction method for embedded system</atitle><jtitle>Microprocessors and microsystems</jtitle><date>2020-03</date><risdate>2020</risdate><volume>73</volume><spage>102957</spage><pages>102957-</pages><artnum>102957</artnum><issn>0141-9331</issn><eissn>1872-9436</eissn><abstract>Speech enhancement aims at improving the overall speech signal perceptual quality using different audio signal processing techniques. Filtering techniques, spectral restoration and model-based methods are the three fundamental classes of speech enhancement algorithms for noise reduction. With mobile devices increased usage, the demand for algorithms processing also increases. As the microphones that capture the voice of the speaker are at considerable distance in most of the devices, noise are getting added thereby degrading the quality of the speech signal. No single theorist's stone or minimization standard has been discovered so far for speech enhancement. In this work, we have combined the statistical models along with machine learning algorithm to improve the results thereby resulting in robust communication. Experimentation results on AURORA 2 database shows us improved results over the state of the art methods discussed in the literature.</abstract><cop>Kidlington</cop><pub>Elsevier B.V</pub><doi>10.1016/j.micpro.2019.102957</doi></addata></record>
fulltext fulltext
identifier ISSN: 0141-9331
ispartof Microprocessors and microsystems, 2020-03, Vol.73, p.102957, Article 102957
issn 0141-9331
1872-9436
language eng
recordid cdi_proquest_journals_2377330862
source Access via ScienceDirect (Elsevier)
subjects Algorithms
Electronic devices
Embedded systems
Experimentation
Linear models
Machine learning
Microphones
Noise reduction
Precision and recall
Restoration
Signal processing
Signal quality
Speech enhancement
Speech processing
Statistical models
Subtraction
Support vector machines
Voice communication
title Combining statistical models using modified spectral subtraction method for embedded system
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-25T13%3A19%3A21IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Combining%20statistical%20models%20using%20modified%20spectral%20subtraction%20method%20for%20embedded%20system&rft.jtitle=Microprocessors%20and%20microsystems&rft.au=Balaji,%20V.R.&rft.date=2020-03&rft.volume=73&rft.spage=102957&rft.pages=102957-&rft.artnum=102957&rft.issn=0141-9331&rft.eissn=1872-9436&rft_id=info:doi/10.1016/j.micpro.2019.102957&rft_dat=%3Cproquest_cross%3E2377330862%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2377330862&rft_id=info:pmid/&rft_els_id=S0141933119304600&rfr_iscdi=true