{"id":"https://openalex.org/W2075059283","doi":"https://doi.org/10.1155/2010/808312","title":"A Ramp Cosine Cepstrum Model for the Parameter Estimation of Autoregressive Systems at Low SNR","display_name":"A Ramp Cosine Cepstrum Model for the Parameter Estimation of Autoregressive Systems at Low SNR","publication_year":2010,"publication_date":"2010-05-20","ids":{"openalex":"https://openalex.org/W2075059283","doi":"https://doi.org/10.1155/2010/808312","mag":"2075059283"},"language":"en","primary_location":{"id":"doi:10.1155/2010/808312","is_oa":true,"landing_page_url":"https://doi.org/10.1155/2010/808312","pdf_url":"https://asp-eurasipjournals.springeropen.com/counter/pdf/10.1155/2010/808312","source":{"id":"https://openalex.org/S35920007","display_name":"EURASIP Journal on Advances in Signal Processing","issn_l":"1687-6172","issn":["1687-6172","1687-6180"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"EURASIP Journal on Advances in Signal Processing","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://asp-eurasipjournals.springeropen.com/counter/pdf/10.1155/2010/808312","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5111820276","display_name":"ShaikhAnowarul Fattah","orcid":null},"institutions":[{"id":"https://openalex.org/I60158472","display_name":"Concordia University","ror":"https://ror.org/0420zvk78","country_code":"CA","type":"education","lineage":["https://openalex.org/I60158472"]},{"id":"https://openalex.org/I20089843","display_name":"Princeton University","ror":"https://ror.org/00hx57361","country_code":"US","type":"education","lineage":["https://openalex.org/I20089843"]}],"countries":["CA","US"],"is_corresponding":true,"raw_author_name":"ShaikhAnowarul Fattah","raw_affiliation_strings":["Department of Electrical Engineering, Princeton University, Engineering Quadrangle, Olden Street, Princeton, NJ, 08544, USA","Department of Electrical and Computer Engineering, Concordia University, 1455 De Maisonneuve Blvd. W., Montreal, QC, Canada H3G 1M8","Department of Electrical Engineering, Princeton University, Engineering Quadrangle, Princeton, USA","Department of Electrical and Computer Engineering, Concordia University, Montreal, Canada H3G 1M8"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, Princeton University, Engineering Quadrangle, Olden Street, Princeton, NJ, 08544, USA","institution_ids":["https://openalex.org/I20089843"]},{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Concordia University, 1455 De Maisonneuve Blvd. W., Montreal, QC, Canada H3G 1M8","institution_ids":["https://openalex.org/I60158472"]},{"raw_affiliation_string":"Department of Electrical Engineering, Princeton University, Engineering Quadrangle, Princeton, USA","institution_ids":["https://openalex.org/I20089843"]},{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Concordia University, Montreal, Canada H3G 1M8","institution_ids":["https://openalex.org/I60158472"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5033247734","display_name":"Wei\u2010Ping Zhu","orcid":null},"institutions":[{"id":"https://openalex.org/I60158472","display_name":"Concordia University","ror":"https://ror.org/0420zvk78","country_code":"CA","type":"education","lineage":["https://openalex.org/I60158472"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Wei-Ping Zhu","raw_affiliation_strings":["Department of Electrical and Computer Engineering, Concordia University, 1455 De Maisonneuve Blvd. W., Montreal, QC, Canada H3G 1M8","Department of Electrical and Computer Engineering, Concordia University, Montreal, Canada H3G 1M8"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Concordia University, 1455 De Maisonneuve Blvd. W., Montreal, QC, Canada H3G 1M8","institution_ids":["https://openalex.org/I60158472"]},{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Concordia University, Montreal, Canada H3G 1M8","institution_ids":["https://openalex.org/I60158472"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5068820891","display_name":"M. Omair Ahmad","orcid":"https://orcid.org/0000-0002-2924-6659"},"institutions":[{"id":"https://openalex.org/I60158472","display_name":"Concordia University","ror":"https://ror.org/0420zvk78","country_code":"CA","type":"education","lineage":["https://openalex.org/I60158472"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"MOmair Ahmad","raw_affiliation_strings":["Department of Electrical and Computer Engineering, Concordia University, 1455 De Maisonneuve Blvd. W., Montreal, QC, Canada H3G 1M8","Department of Electrical and Computer Engineering, Concordia University, Montreal, Canada H3G 1M8"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Concordia University, 1455 De Maisonneuve Blvd. W., Montreal, QC, Canada H3G 1M8","institution_ids":["https://openalex.org/I60158472"]},{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Concordia University, Montreal, Canada H3G 1M8","institution_ids":["https://openalex.org/I60158472"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5111820276"],"corresponding_institution_ids":["https://openalex.org/I20089843","https://openalex.org/I60158472"],"apc_list":{"value":1140,"currency":"GBP","value_usd":1398},"apc_paid":{"value":1140,"currency":"GBP","value_usd":1398},"fwci":0.0,"has_fulltext":true,"cited_by_count":1,"citation_normalized_percentile":{"value":0.15270184,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":"2010","issue":"1","first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10860","display_name":"Speech and Audio Processing","score":1.0,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10860","display_name":"Speech and Audio Processing","score":1.0,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11233","display_name":"Advanced Adaptive Filtering Techniques","score":0.9993000030517578,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11447","display_name":"Blind Source Separation Techniques","score":0.9991999864578247,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/cepstrum","display_name":"Cepstrum","score":0.8523324131965637},{"id":"https://openalex.org/keywords/autoregressive-model","display_name":"Autoregressive model","score":0.7625435590744019},{"id":"https://openalex.org/keywords/discrete-cosine-transform","display_name":"Discrete cosine transform","score":0.6782848834991455},{"id":"https://openalex.org/keywords/autocorrelation","display_name":"Autocorrelation","score":0.6526879668235779},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6188353300094604},{"id":"https://openalex.org/keywords/discrete-fourier-transform","display_name":"Discrete Fourier transform (general)","score":0.5249538421630859},{"id":"https://openalex.org/keywords/estimation-theory","display_name":"Estimation theory","score":0.5214314460754395},{"id":"https://openalex.org/keywords/white-noise","display_name":"White noise","score":0.5171715617179871},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.49907565116882324},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.49416831135749817},{"id":"https://openalex.org/keywords/spectral-density","display_name":"Spectral density","score":0.4804096519947052},{"id":"https://openalex.org/keywords/spectral-density-estimation","display_name":"Spectral density estimation","score":0.46614256501197815},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.4421579837799072},{"id":"https://openalex.org/keywords/system-identification","display_name":"System identification","score":0.43465909361839294},{"id":"https://openalex.org/keywords/impulse-response","display_name":"Impulse response","score":0.42921534180641174},{"id":"https://openalex.org/keywords/fourier-transform","display_name":"Fourier transform","score":0.3487880825996399},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.33549201488494873},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.17933985590934753},{"id":"https://openalex.org/keywords/fourier-analysis","display_name":"Fourier analysis","score":0.1483379304409027},{"id":"https://openalex.org/keywords/fractional-fourier-transform","display_name":"Fractional Fourier transform","score":0.14390406012535095},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.11057549715042114},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.10982218384742737},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.1034325659275055}],"concepts":[{"id":"https://openalex.org/C88485024","wikidata":"https://www.wikidata.org/wiki/Q1054571","display_name":"Cepstrum","level":2,"score":0.8523324131965637},{"id":"https://openalex.org/C159877910","wikidata":"https://www.wikidata.org/wiki/Q2202883","display_name":"Autoregressive model","level":2,"score":0.7625435590744019},{"id":"https://openalex.org/C2221639","wikidata":"https://www.wikidata.org/wiki/Q2877","display_name":"Discrete cosine transform","level":3,"score":0.6782848834991455},{"id":"https://openalex.org/C5297727","wikidata":"https://www.wikidata.org/wiki/Q786970","display_name":"Autocorrelation","level":2,"score":0.6526879668235779},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6188353300094604},{"id":"https://openalex.org/C57733114","wikidata":"https://www.wikidata.org/wiki/Q1006032","display_name":"Discrete Fourier transform (general)","level":5,"score":0.5249538421630859},{"id":"https://openalex.org/C167928553","wikidata":"https://www.wikidata.org/wiki/Q1376021","display_name":"Estimation theory","level":2,"score":0.5214314460754395},{"id":"https://openalex.org/C112633086","wikidata":"https://www.wikidata.org/wiki/Q381287","display_name":"White noise","level":2,"score":0.5171715617179871},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.49907565116882324},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.49416831135749817},{"id":"https://openalex.org/C168110828","wikidata":"https://www.wikidata.org/wiki/Q1331626","display_name":"Spectral density","level":2,"score":0.4804096519947052},{"id":"https://openalex.org/C30049272","wikidata":"https://www.wikidata.org/wiki/Q6555326","display_name":"Spectral density estimation","level":3,"score":0.46614256501197815},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.4421579837799072},{"id":"https://openalex.org/C119247159","wikidata":"https://www.wikidata.org/wiki/Q1366192","display_name":"System identification","level":3,"score":0.43465909361839294},{"id":"https://openalex.org/C72279823","wikidata":"https://www.wikidata.org/wiki/Q1139726","display_name":"Impulse response","level":2,"score":0.42921534180641174},{"id":"https://openalex.org/C102519508","wikidata":"https://www.wikidata.org/wiki/Q6520159","display_name":"Fourier transform","level":2,"score":0.3487880825996399},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.33549201488494873},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.17933985590934753},{"id":"https://openalex.org/C203024314","wikidata":"https://www.wikidata.org/wiki/Q1365258","display_name":"Fourier analysis","level":3,"score":0.1483379304409027},{"id":"https://openalex.org/C76563020","wikidata":"https://www.wikidata.org/wiki/Q4817582","display_name":"Fractional Fourier transform","level":4,"score":0.14390406012535095},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.11057549715042114},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.10982218384742737},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.1034325659275055},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1155/2010/808312","is_oa":true,"landing_page_url":"https://doi.org/10.1155/2010/808312","pdf_url":"https://asp-eurasipjournals.springeropen.com/counter/pdf/10.1155/2010/808312","source":{"id":"https://openalex.org/S35920007","display_name":"EURASIP Journal on Advances in Signal Processing","issn_l":"1687-6172","issn":["1687-6172","1687-6180"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"EURASIP Journal on Advances in Signal Processing","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:a7ea129e56d14a3a847527a9915c972d","is_oa":true,"landing_page_url":"https://doaj.org/article/a7ea129e56d14a3a847527a9915c972d","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"EURASIP Journal on Advances in Signal Processing, Vol 2010 (2010)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1155/2010/808312","is_oa":true,"landing_page_url":"https://doi.org/10.1155/2010/808312","pdf_url":"https://asp-eurasipjournals.springeropen.com/counter/pdf/10.1155/2010/808312","source":{"id":"https://openalex.org/S35920007","display_name":"EURASIP Journal on Advances in Signal Processing","issn_l":"1687-6172","issn":["1687-6172","1687-6180"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"EURASIP Journal on Advances in Signal Processing","raw_type":"journal-article"},"sustainable_development_goals":[{"score":0.6899999976158142,"id":"https://metadata.un.org/sdg/16","display_name":"Peace, Justice and strong institutions"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320334593","display_name":"Natural Sciences and Engineering Research Council of Canada","ror":"https://ror.org/01h531d29"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2075059283.pdf","grobid_xml":"https://content.openalex.org/works/W2075059283.grobid-xml"},"referenced_works_count":25,"referenced_works":["https://openalex.org/W1597732836","https://openalex.org/W1766888123","https://openalex.org/W1974387177","https://openalex.org/W1978656032","https://openalex.org/W1983458885","https://openalex.org/W1990826500","https://openalex.org/W1998649069","https://openalex.org/W2012254086","https://openalex.org/W2039278797","https://openalex.org/W2068154308","https://openalex.org/W2070696251","https://openalex.org/W2097143083","https://openalex.org/W2109770219","https://openalex.org/W2112690556","https://openalex.org/W2113486168","https://openalex.org/W2114548037","https://openalex.org/W2116255192","https://openalex.org/W2131092804","https://openalex.org/W2135610743","https://openalex.org/W2138056643","https://openalex.org/W2147676440","https://openalex.org/W2163192375","https://openalex.org/W2169110040","https://openalex.org/W2485688913","https://openalex.org/W3127686677"],"related_works":["https://openalex.org/W2099586261","https://openalex.org/W2163192375","https://openalex.org/W2115834725","https://openalex.org/W237152309","https://openalex.org/W2018802643","https://openalex.org/W1578593028","https://openalex.org/W3012144689","https://openalex.org/W2149566846","https://openalex.org/W2139670521","https://openalex.org/W2391642628"],"abstract_inverted_index":{"A":[0,25],"new":[1],"cosine":[2,27,90],"cepstrum":[3,28],"model-based":[4],"scheme":[5],"is":[6,41,69,111,123,129],"presented":[7,71],"for":[8,31,145,167],"the":[9,32,54,64,67,76,84,88,96,107,126,142,146,159,168,189,194],"parameter":[10],"estimation":[11,184],"of":[12,21,37,66,86,119,131,141,158,170,188,193],"a":[13,57,155,171,182],"minimum-phase":[14],"autoregressive":[15],"(AR)":[16],"system":[17,68,175],"under":[18],"low":[19,150],"levels":[20,148],"signal-to-noise":[22],"ratio":[23],"(SNR).":[24],"ramp":[26],"(RCC)":[29],"model":[30],"one-sided":[33],"autocorrelation":[34],"function":[35],"(OSACF)":[36],"an":[38],"AR":[39,77,117],"signal":[40],"first":[42],"proposed":[43,127,160],"by":[44],"considering":[45],"both":[46],"white":[47],"noise":[48],"and":[49,100,135],"periodic":[50],"impulse-train":[51],"excitations.":[52],"Using":[53],"RCC":[55],"model,":[56],"residue-based":[58],"least-squares":[59],"optimization":[60],"technique":[61],"that":[62,125],"guarantees":[63],"stability":[65],"then":[70],"in":[72,137,186],"order":[73],"to":[74,106,139],"estimate":[75],"parameters":[78,133],"from":[79],"noisy":[80],"output":[81],"observations.":[82],"For":[83],"purpose":[85],"implementation,":[87],"discrete":[89,108],"transform,":[91,110],"which":[92],"can":[93],"efficiently":[94],"handle":[95],"phase":[97],"unwrapping":[98],"problem":[99],"offer":[101],"computational":[102],"advantages":[103],"as":[104,149,151],"compared":[105],"Fourier":[109],"employed.":[112],"From":[113],"extensive":[114],"experimentations":[115],"on":[116],"systems":[118],"different":[120],"orders,":[121],"it":[122],"shown":[124],"method":[128],"capable":[130],"estimating":[132],"accurately":[134],"consistently":[136],"comparison":[138],"some":[140],"existing":[143],"methods":[144],"SNR":[147],"\u22125":[152],"dB.":[153],"As":[154],"practical":[156],"application":[157],"technique,":[161],"simulation":[162],"results":[163],"are":[164],"also":[165],"provided":[166],"identification":[169],"human":[172],"vocal":[173],"tract":[174],"using":[176],"noise-corrupted":[177],"natural":[178],"speech":[179,196],"signals":[180],"demonstrating":[181],"superior":[183],"performance":[185],"terms":[187],"power":[190],"spectral":[191],"density":[192],"synthesized":[195],"signals.":[197]},"counts_by_year":[{"year":2023,"cited_by_count":1}],"updated_date":"2026-05-06T08:25:59.206177","created_date":"2016-06-24T00:00:00"}
