{"id":"https://openalex.org/W4415319701","doi":"https://doi.org/10.1007/s00034-025-03361-w","title":"Methodologies Incorporating Cepstral Nulling for Time Series Cepstral Dissimilarity Evaluation","display_name":"Methodologies Incorporating Cepstral Nulling for Time Series Cepstral Dissimilarity Evaluation","publication_year":2025,"publication_date":"2025-10-18","ids":{"openalex":"https://openalex.org/W4415319701","doi":"https://doi.org/10.1007/s00034-025-03361-w"},"language":"en","primary_location":{"id":"doi:10.1007/s00034-025-03361-w","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s00034-025-03361-w","pdf_url":"https://link.springer.com/content/pdf/10.1007/s00034-025-03361-w.pdf","source":{"id":"https://openalex.org/S20109229","display_name":"Circuits Systems and Signal Processing","issn_l":"0278-081X","issn":["0278-081X","1531-5878"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320186","host_organization_name":"Birkh\u00e4user","host_organization_lineage":["https://openalex.org/P4310320186","https://openalex.org/P4310319900"],"host_organization_lineage_names":["Birkh\u00e4user","Springer Science+Business Media"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Circuits, Systems, and Signal Processing","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://link.springer.com/content/pdf/10.1007/s00034-025-03361-w.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5017679200","display_name":"Miaotian Li","orcid":null},"institutions":[{"id":"https://openalex.org/I154130895","display_name":"University of Auckland","ror":"https://ror.org/03b94tp07","country_code":"NZ","type":"education","lineage":["https://openalex.org/I154130895"]}],"countries":["NZ"],"is_corresponding":false,"raw_author_name":"Miaotian Li","raw_affiliation_strings":["Department of Statistics, University of Auckland, Auckland, 1010, New Zealand"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Statistics, University of Auckland, Auckland, 1010, New Zealand","institution_ids":["https://openalex.org/I154130895"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5006344024","display_name":"Ciprian Doru Giurc\u0103neanu","orcid":"https://orcid.org/0000-0001-5512-0868"},"institutions":[{"id":"https://openalex.org/I154130895","display_name":"University of Auckland","ror":"https://ror.org/03b94tp07","country_code":"NZ","type":"education","lineage":["https://openalex.org/I154130895"]}],"countries":["NZ"],"is_corresponding":true,"raw_author_name":"Ciprian Doru Giurc\u0103neanu","raw_affiliation_strings":["Department of Statistics, University of Auckland, Auckland, 1010, New Zealand"],"raw_orcid":"https://orcid.org/0000-0001-5512-0868","affiliations":[{"raw_affiliation_string":"Department of Statistics, University of Auckland, Auckland, 1010, New Zealand","institution_ids":["https://openalex.org/I154130895"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5006344024"],"corresponding_institution_ids":["https://openalex.org/I154130895"],"apc_list":{"value":3190,"currency":"USD","value_usd":3190},"apc_paid":{"value":3190,"currency":"USD","value_usd":3190},"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.27451349,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"45","issue":"5","first_page":"3700","last_page":"3727"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9995999932289124,"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/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9995999932289124,"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/T11447","display_name":"Blind Source Separation Techniques","score":0.991100013256073,"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/T13487","display_name":"Statistical and numerical algorithms","score":0.9889000058174133,"subfield":{"id":"https://openalex.org/subfields/2604","display_name":"Applied Mathematics"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/cepstrum","display_name":"Cepstrum","score":0.8646000027656555},{"id":"https://openalex.org/keywords/mel-frequency-cepstrum","display_name":"Mel-frequency cepstrum","score":0.7429999709129333},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.6342999935150146},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.6171000003814697},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.5090000033378601},{"id":"https://openalex.org/keywords/autoregressive-model","display_name":"Autoregressive model","score":0.5030999779701233},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4876999855041504},{"id":"https://openalex.org/keywords/mean-squared-error","display_name":"Mean squared error","score":0.45910000801086426}],"concepts":[{"id":"https://openalex.org/C88485024","wikidata":"https://www.wikidata.org/wiki/Q1054571","display_name":"Cepstrum","level":2,"score":0.8646000027656555},{"id":"https://openalex.org/C151989614","wikidata":"https://www.wikidata.org/wiki/Q440370","display_name":"Mel-frequency cepstrum","level":3,"score":0.7429999709129333},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.6342999935150146},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.6171000003814697},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5090000033378601},{"id":"https://openalex.org/C159877910","wikidata":"https://www.wikidata.org/wiki/Q2202883","display_name":"Autoregressive model","level":2,"score":0.5030999779701233},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.4968000054359436},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4876999855041504},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.45910000801086426},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.4530999958515167},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.444599986076355},{"id":"https://openalex.org/C168136583","wikidata":"https://www.wikidata.org/wiki/Q1988242","display_name":"Bayesian information criterion","level":2,"score":0.41679999232292175},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.37700000405311584},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3596999943256378},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.3531000018119812},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.3506999909877777},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3386000096797943},{"id":"https://openalex.org/C24338571","wikidata":"https://www.wikidata.org/wiki/Q2566298","display_name":"Autoregressive integrated moving average","level":3,"score":0.33480000495910645},{"id":"https://openalex.org/C167928553","wikidata":"https://www.wikidata.org/wiki/Q1376021","display_name":"Estimation theory","level":2,"score":0.3034999966621399},{"id":"https://openalex.org/C4978587","wikidata":"https://www.wikidata.org/wiki/Q1138810","display_name":"Cram\u00e9r\u2013Rao bound","level":3,"score":0.2903999984264374},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.2736999988555908},{"id":"https://openalex.org/C40969351","wikidata":"https://www.wikidata.org/wiki/Q3516228","display_name":"Word error rate","level":2,"score":0.2639000117778778},{"id":"https://openalex.org/C71907059","wikidata":"https://www.wikidata.org/wiki/Q223323","display_name":"Root mean square","level":2,"score":0.25780001282691956}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1007/s00034-025-03361-w","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s00034-025-03361-w","pdf_url":"https://link.springer.com/content/pdf/10.1007/s00034-025-03361-w.pdf","source":{"id":"https://openalex.org/S20109229","display_name":"Circuits Systems and Signal Processing","issn_l":"0278-081X","issn":["0278-081X","1531-5878"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320186","host_organization_name":"Birkh\u00e4user","host_organization_lineage":["https://openalex.org/P4310320186","https://openalex.org/P4310319900"],"host_organization_lineage_names":["Birkh\u00e4user","Springer Science+Business Media"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Circuits, Systems, and Signal Processing","raw_type":"journal-article"},{"id":"pmh:oai:researchspace.auckland.ac.nz:2292/73953","is_oa":true,"landing_page_url":"https://hdl.handle.net/2292/73953","pdf_url":null,"source":{"id":"https://openalex.org/S7407055463","display_name":"ResearchSpace (University of Auckland)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I154130895","host_organization_name":"University of Auckland","host_organization_lineage":["https://openalex.org/I154130895"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Journal Article"}],"best_oa_location":{"id":"doi:10.1007/s00034-025-03361-w","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s00034-025-03361-w","pdf_url":"https://link.springer.com/content/pdf/10.1007/s00034-025-03361-w.pdf","source":{"id":"https://openalex.org/S20109229","display_name":"Circuits Systems and Signal Processing","issn_l":"0278-081X","issn":["0278-081X","1531-5878"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320186","host_organization_name":"Birkh\u00e4user","host_organization_lineage":["https://openalex.org/P4310320186","https://openalex.org/P4310319900"],"host_organization_lineage_names":["Birkh\u00e4user","Springer Science+Business Media"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Circuits, Systems, and Signal Processing","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320320801","display_name":"University of Auckland","ror":"https://ror.org/03b94tp07"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4415319701.pdf","grobid_xml":"https://content.openalex.org/works/W4415319701.grobid-xml"},"referenced_works_count":44,"referenced_works":["https://openalex.org/W131856359","https://openalex.org/W210359992","https://openalex.org/W1540089290","https://openalex.org/W1607202247","https://openalex.org/W1963737619","https://openalex.org/W1986783130","https://openalex.org/W1989919041","https://openalex.org/W1991567646","https://openalex.org/W1999955319","https://openalex.org/W2009587140","https://openalex.org/W2015416689","https://openalex.org/W2034230784","https://openalex.org/W2045666828","https://openalex.org/W2054708957","https://openalex.org/W2069649755","https://openalex.org/W2086621728","https://openalex.org/W2100718094","https://openalex.org/W2110065044","https://openalex.org/W2114618801","https://openalex.org/W2127180234","https://openalex.org/W2132914434","https://openalex.org/W2135658083","https://openalex.org/W2136063736","https://openalex.org/W2150593711","https://openalex.org/W2162800060","https://openalex.org/W2483631512","https://openalex.org/W2582697902","https://openalex.org/W2593221019","https://openalex.org/W2933561437","https://openalex.org/W2949263148","https://openalex.org/W2970988466","https://openalex.org/W2979983642","https://openalex.org/W3041020092","https://openalex.org/W3103980728","https://openalex.org/W3131078466","https://openalex.org/W3183441926","https://openalex.org/W4230751662","https://openalex.org/W4256479478","https://openalex.org/W4300544062","https://openalex.org/W4312409121","https://openalex.org/W4365511600","https://openalex.org/W4401002871","https://openalex.org/W4406865777","https://openalex.org/W6959554965"],"related_works":[],"abstract_inverted_index":{"Abstract":[0],"The":[1,40,174],"most":[2,50,98],"popular":[3],"cepstral":[4,21,82,95,111,147,179,199,206,233],"distances":[5,207],"are":[6,26,42,53,235],"computed":[7],"as":[8,94],"the":[9,13,20,24,29,67,72,80,103,116,159,193,196,202,205,212,225,232,238],"square":[10],"root":[11],"of":[12,16,19,51,79,105,176,195,198,204,211],"weighted":[14],"sum":[15],"squared":[17],"differences":[18],"coefficients.":[22,68],"Traditionally,":[23],"coefficients":[25,83,234],"estimated":[27,81],"from":[28],"time":[30,89],"series":[31],"measurements":[32],"using":[33,237],"autoregressive":[34],"integrated":[35],"moving":[36],"average":[37],"(ARIMA)":[38],"models.":[39],"weights":[41,73],"also":[43,191],"derived":[44],"based":[45,114],"on":[46,115],"ARIMA":[47],"models,":[48],"and":[49,133,155,166],"them":[52],"empirically":[54],"set":[55,77],"to":[56,65,74,84,146],"zero.":[57],"In":[58,215],"this":[59],"study,":[60],"we":[61,76,150,161,190,222],"adopt":[62],"non-parametric":[63],"methods":[64,181],"estimate":[66],"Rather":[69],"than":[70],"setting":[71],"zero,":[75],"some":[78],"zero":[85],"independently":[86],"for":[87,164],"each":[88],"series.":[90],"This":[91],"procedure,":[92],"known":[93],"nulling,":[96],"has":[97],"commonly":[99],"been":[100,143],"applied":[101,145],"in":[102,184,201,209],"context":[104],"periodogram":[106],"smoothing.":[107],"We":[108],"explore":[109],"five":[110,178],"nulling":[112,180,200],"approaches":[113],"Bayesian":[117],"Information":[118],"Criterion":[119],"(BIC),":[120],"Minimum":[121],"Risk":[122],"Inflation":[123],"(MRI),":[124],"Kolmogorov":[125],"Structure":[126],"Function":[127],"(KSF),":[128],"False":[129],"Discovery":[130],"Rate":[131,136],"(FDR),":[132],"Familywise":[134],"Error":[135],"(FER).":[137],"As":[138],"these":[139],"techniques":[140],"have":[141],"not":[142],"previously":[144],"distance":[148],"estimation,":[149],"evaluate":[151],"their":[152,172],"performance":[153,175],"theoretically":[154],"empirically.":[156],"To":[157],"streamline":[158],"analysis,":[160],"prove":[162],"only":[163],"BIC":[165],"MRI":[167,239],"asymptotic":[168],"results":[169],"that":[170,224],"elucidate":[171],"performance.":[173],"all":[177],"is":[182,229],"demonstrated":[183],"experiments":[185,216],"with":[186,217],"simulated":[187,213],"data.":[188,214],"Furthermore,":[189],"assess":[192],"effect":[194],"use":[197],"evaluation":[203],"employed":[208],"clustering":[210,227],"real-life":[218],"data":[219],"(ECG":[220],"signals),":[221],"show":[223],"best":[226],"result":[228],"achieved":[230],"when":[231],"thresholded":[236],"method.":[240]},"counts_by_year":[],"updated_date":"2026-08-26T07:47:46.906454","created_date":"2025-10-19T00:00:00"}
