{"id":"https://openalex.org/W2475100461","doi":"https://doi.org/10.1109/tsp.2016.2585091","title":"Aliasing Reduction in Clipped Signals","display_name":"Aliasing Reduction in Clipped Signals","publication_year":2016,"publication_date":"2016-06-24","ids":{"openalex":"https://openalex.org/W2475100461","doi":"https://doi.org/10.1109/tsp.2016.2585091","mag":"2475100461"},"language":"en","primary_location":{"id":"doi:10.1109/tsp.2016.2585091","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tsp.2016.2585091","pdf_url":null,"source":{"id":"https://openalex.org/S168680287","display_name":"IEEE Transactions on Signal Processing","issn_l":"1053-587X","issn":["1053-587X","1941-0476"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Signal Processing","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://www.research.ed.ac.uk/files/26997332/07499828.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5007695842","display_name":"Fabi\u00e1n Esqueda","orcid":"https://orcid.org/0000-0003-1753-3084"},"institutions":[{"id":"https://openalex.org/I9927081","display_name":"Aalto University","ror":"https://ror.org/020hwjq30","country_code":"FI","type":"education","lineage":["https://openalex.org/I9927081"]}],"countries":["FI"],"is_corresponding":false,"raw_author_name":"Fabian Esqueda","raw_affiliation_strings":["Department of Signal Processing and Acoustics, AALTO, Espoo, FI, Finland"],"raw_orcid":"https://orcid.org/0000-0003-1753-3084","affiliations":[{"raw_affiliation_string":"Department of Signal Processing and Acoustics, AALTO, Espoo, FI, Finland","institution_ids":["https://openalex.org/I9927081"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5023116667","display_name":"Stefan Bilbao","orcid":"https://orcid.org/0000-0001-5332-9887"},"institutions":[{"id":"https://openalex.org/I98677209","display_name":"University of Edinburgh","ror":"https://ror.org/01nrxwf90","country_code":"GB","type":"education","lineage":["https://openalex.org/I98677209"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Stefan Bilbao","raw_affiliation_strings":["Acoustics and Audio Group, University of Edinburgh, Edinburgh, U.K"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Acoustics and Audio Group, University of Edinburgh, Edinburgh, U.K","institution_ids":["https://openalex.org/I98677209"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5021963945","display_name":"Vesa V\u00e4lim\u00e4ki","orcid":"https://orcid.org/0000-0002-7869-292X"},"institutions":[{"id":"https://openalex.org/I9927081","display_name":"Aalto University","ror":"https://ror.org/020hwjq30","country_code":"FI","type":"education","lineage":["https://openalex.org/I9927081"]}],"countries":["FI"],"is_corresponding":false,"raw_author_name":"Vesa Valimaki","raw_affiliation_strings":["Department of Signal Processing and Acoustics, AALTO, Espoo, FI, Finland"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Signal Processing and Acoustics, AALTO, Espoo, FI, Finland","institution_ids":["https://openalex.org/I9927081"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":3.4936,"has_fulltext":true,"cited_by_count":54,"citation_normalized_percentile":{"value":0.93491392,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":"64","issue":"20","first_page":"5255","last_page":"5267"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10688","display_name":"Image and Signal Denoising Methods","score":0.9993000030517578,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T10688","display_name":"Image and Signal Denoising Methods","score":0.9993000030517578,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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":0.9991000294685364,"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/T11034","display_name":"Digital Filter Design and Implementation","score":0.9990000128746033,"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/oversampling","display_name":"Oversampling","score":0.6740934252738953},{"id":"https://openalex.org/keywords/aliasing","display_name":"Aliasing","score":0.6440877914428711},{"id":"https://openalex.org/keywords/bandlimiting","display_name":"Bandlimiting","score":0.6267359256744385},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.5884868502616882},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.5568464994430542},{"id":"https://openalex.org/keywords/clipping","display_name":"Clipping (morphology)","score":0.5507914423942566},{"id":"https://openalex.org/keywords/waveform","display_name":"Waveform","score":0.4349895417690277},{"id":"https://openalex.org/keywords/nyquist-frequency","display_name":"Nyquist frequency","score":0.43250083923339844},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.3718540668487549},{"id":"https://openalex.org/keywords/undersampling","display_name":"Undersampling","score":0.26694434881210327},{"id":"https://openalex.org/keywords/fourier-transform","display_name":"Fourier transform","score":0.24676230549812317},{"id":"https://openalex.org/keywords/bandwidth","display_name":"Bandwidth (computing)","score":0.21167638897895813},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.1825467348098755},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.12262403964996338},{"id":"https://openalex.org/keywords/mathematical-analysis","display_name":"Mathematical analysis","score":0.1053743064403534}],"concepts":[{"id":"https://openalex.org/C197323446","wikidata":"https://www.wikidata.org/wiki/Q331222","display_name":"Oversampling","level":3,"score":0.6740934252738953},{"id":"https://openalex.org/C4069607","wikidata":"https://www.wikidata.org/wiki/Q868732","display_name":"Aliasing","level":3,"score":0.6440877914428711},{"id":"https://openalex.org/C129997835","wikidata":"https://www.wikidata.org/wiki/Q806263","display_name":"Bandlimiting","level":3,"score":0.6267359256744385},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5884868502616882},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.5568464994430542},{"id":"https://openalex.org/C2776848632","wikidata":"https://www.wikidata.org/wiki/Q853463","display_name":"Clipping (morphology)","level":2,"score":0.5507914423942566},{"id":"https://openalex.org/C197424946","wikidata":"https://www.wikidata.org/wiki/Q1165717","display_name":"Waveform","level":3,"score":0.4349895417690277},{"id":"https://openalex.org/C98273374","wikidata":"https://www.wikidata.org/wiki/Q1501757","display_name":"Nyquist frequency","level":3,"score":0.43250083923339844},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.3718540668487549},{"id":"https://openalex.org/C136536468","wikidata":"https://www.wikidata.org/wiki/Q1225894","display_name":"Undersampling","level":2,"score":0.26694434881210327},{"id":"https://openalex.org/C102519508","wikidata":"https://www.wikidata.org/wiki/Q6520159","display_name":"Fourier transform","level":2,"score":0.24676230549812317},{"id":"https://openalex.org/C2776257435","wikidata":"https://www.wikidata.org/wiki/Q1576430","display_name":"Bandwidth (computing)","level":2,"score":0.21167638897895813},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.1825467348098755},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.12262403964996338},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.1053743064403534},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C554190296","wikidata":"https://www.wikidata.org/wiki/Q47528","display_name":"Radar","level":2,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.1109/tsp.2016.2585091","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tsp.2016.2585091","pdf_url":null,"source":{"id":"https://openalex.org/S168680287","display_name":"IEEE Transactions on Signal Processing","issn_l":"1053-587X","issn":["1053-587X","1941-0476"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Signal Processing","raw_type":"journal-article"},{"id":"pmh:oai:pure.ed.ac.uk:openaire/e2140225-4195-44ea-bf05-b9332a818abc","is_oa":true,"landing_page_url":"https://www.research.ed.ac.uk/en/publications/e2140225-4195-44ea-bf05-b9332a818abc","pdf_url":"https://www.research.ed.ac.uk/files/26997332/07499828.pdf","source":{"id":"https://openalex.org/S4306400321","display_name":"Edinburgh Research Explorer (University of Edinburgh)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I98677209","host_organization_name":"University of Edinburgh","host_organization_lineage":["https://openalex.org/I98677209"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Esqueda, F, Bilbao, S & Valimaki, V 2016, 'Aliasing reduction in clipped signals', IEEE Transactions on Signal Processing, vol. 64, no. 20, pp. 5255-5267. https://doi.org/10.1109/TSP.2016.2585091","raw_type":"info:eu-repo/semantics/article"},{"id":"pmh:oai:pure.ed.ac.uk:publications/e2140225-4195-44ea-bf05-b9332a818abc","is_oa":true,"landing_page_url":"http://hdl.handle.net/20.500.11820/e2140225-4195-44ea-bf05-b9332a818abc","pdf_url":"https://www.pure.ed.ac.uk/ws/files/26997332/07499828.pdf","source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":""},{"id":"pmh:oai:pure.ed.ac.uk:publications/e2140225-4195-44ea-bf05-b9332a818abc","is_oa":true,"landing_page_url":"https://hdl.handle.net/20.500.11820/e2140225-4195-44ea-bf05-b9332a818abc","pdf_url":"https://www.pure.ed.ac.uk/ws/files/26997332/07499828.pdf","source":{"id":"https://openalex.org/S4306400321","display_name":"Edinburgh Research Explorer (University of Edinburgh)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I98677209","host_organization_name":"University of Edinburgh","host_organization_lineage":["https://openalex.org/I98677209"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Esqueda, F, Bilbao, S & Valimaki, V 2016, 'Aliasing reduction in clipped signals', IEEE Transactions on Signal Processing, vol. 64, no. 20, pp. 5255-5267. https://doi.org/10.1109/TSP.2016.2585091","raw_type":"info:eu-repo/semantics/article"}],"best_oa_location":{"id":"pmh:oai:pure.ed.ac.uk:openaire/e2140225-4195-44ea-bf05-b9332a818abc","is_oa":true,"landing_page_url":"https://www.research.ed.ac.uk/en/publications/e2140225-4195-44ea-bf05-b9332a818abc","pdf_url":"https://www.research.ed.ac.uk/files/26997332/07499828.pdf","source":{"id":"https://openalex.org/S4306400321","display_name":"Edinburgh Research Explorer (University of Edinburgh)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I98677209","host_organization_name":"University of Edinburgh","host_organization_lineage":["https://openalex.org/I98677209"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Esqueda, F, Bilbao, S & Valimaki, V 2016, 'Aliasing reduction in clipped signals', IEEE Transactions on Signal Processing, vol. 64, no. 20, pp. 5255-5267. https://doi.org/10.1109/TSP.2016.2585091","raw_type":"info:eu-repo/semantics/article"},"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320324186","display_name":"S\u00e4hk\u00f6tekniikan Korkeakoulu, Aalto-yliopisto","ror":"https://ror.org/020hwjq30"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2475100461.pdf","grobid_xml":"https://content.openalex.org/works/W2475100461.grobid-xml"},"referenced_works_count":43,"referenced_works":["https://openalex.org/W96364143","https://openalex.org/W97530535","https://openalex.org/W108850305","https://openalex.org/W1489573402","https://openalex.org/W1506227094","https://openalex.org/W1537891693","https://openalex.org/W1546808212","https://openalex.org/W1547021691","https://openalex.org/W1552959634","https://openalex.org/W1598466313","https://openalex.org/W1981534370","https://openalex.org/W2003410902","https://openalex.org/W2009844322","https://openalex.org/W2029362727","https://openalex.org/W2035826116","https://openalex.org/W2057606938","https://openalex.org/W2057845019","https://openalex.org/W2086280960","https://openalex.org/W2100848631","https://openalex.org/W2103525130","https://openalex.org/W2112390905","https://openalex.org/W2112709763","https://openalex.org/W2127851351","https://openalex.org/W2129857175","https://openalex.org/W2155595938","https://openalex.org/W2172909489","https://openalex.org/W2222562092","https://openalex.org/W2296153915","https://openalex.org/W2314485382","https://openalex.org/W2332598285","https://openalex.org/W2339173332","https://openalex.org/W2485738731","https://openalex.org/W2914537008","https://openalex.org/W2951920181","https://openalex.org/W4229897592","https://openalex.org/W4247013663","https://openalex.org/W4285719527","https://openalex.org/W6603909836","https://openalex.org/W6603952061","https://openalex.org/W6604482657","https://openalex.org/W6629414630","https://openalex.org/W6633312919","https://openalex.org/W6894276050"],"related_works":["https://openalex.org/W2098726873","https://openalex.org/W2122502139","https://openalex.org/W2100912434","https://openalex.org/W2160627876","https://openalex.org/W2315093958","https://openalex.org/W2023356733","https://openalex.org/W2170356637","https://openalex.org/W2020453585","https://openalex.org/W4242173009","https://openalex.org/W2132752624"],"abstract_inverted_index":{"An":[0,182],"aliasing":[1,140,180,227],"reduction":[2],"method":[3,59,86,135],"for":[4,46,120],"hard-clipped":[5],"sampled":[6],"signals":[7,130],"is":[8,23,52,175,192],"proposed.":[9],"Clipping":[10],"in":[11,67,74,139,160,203,211],"the":[12,31,37,42,68,75,81,90,94,100,104,114,133,152,176,186,200,204],"digital":[13,220],"domain":[14],"causes":[15],"a":[16,57,71],"large":[17],"amount":[18],"of":[19,65,70,89,93,103,113,185,216],"harmonic":[20,129],"distortion,":[21],"which":[22,54,174],"not":[24,196],"bandlimited,":[25],"so":[26],"spectral":[27],"components":[28],"generated":[29],"above":[30],"Nyquist":[32],"limit":[33],"are":[34,77,118,164,209],"reflected":[35],"to":[36,56,60,79,179],"baseband":[38],"and":[39,99,128,145,157,163,168,213],"mixed":[40],"with":[41],"signal.":[43],"A":[44,63],"model":[45],"an":[47],"ideal":[48],"bandlimited":[49,115],"ramp":[50,116],"function":[51,117],"derived,":[53],"leads":[55],"postprocessing":[58],"reduce":[61],"aliasing.":[62],"number":[64],"samples":[66,98],"neighborhood":[69],"clipping":[72,95,201],"point":[73,96],"waveform":[76],"modified":[78],"simulate":[80],"Gibbs":[82],"phenomenon.":[83],"This":[84],"novel":[85],"requires":[87],"estimation":[88],"fractional":[91],"delay":[92],"between":[97],"first":[101],"derivative":[102],"original":[105],"signal":[106,217,221],"at":[107],"that":[108,132,193],"point.":[109],"Two":[110],"polynomial":[111,147,187],"approximations":[112],"suggested":[119],"practical":[121],"implementation.":[122],"Validation":[123],"tests":[124],"using":[125],"sinusoidal,":[126],"triangular,":[127],"show":[131],"proposed":[134,143,207],"achieves":[136],"high":[137],"accuracy":[138],"reduction.":[141,181],"The":[142,206],"2-point":[144],"4-point":[146],"correction":[148,188],"methods":[149,189],"can":[150],"improve":[151],"signal-to-noise":[153],"ratio":[154],"by":[155],"12":[156],"20":[158],"dB":[159],"average,":[161],"respectively,":[162],"more":[165],"computationally":[166],"efficient":[167],"cause":[169],"less":[170],"latency":[171],"than":[172],"oversampling,":[173],"standard":[177],"approach":[178],"additional":[183],"advantage":[184],"over":[190],"oversampling":[191],"they":[194],"do":[195],"introduce":[197],"overshoot":[198],"beyond":[199],"level":[202],"waveform.":[205],"techniques":[208],"useful":[210],"audio":[212],"other":[214],"fields":[215],"processing":[218],"where":[219],"values":[222],"must":[223],"be":[224,229],"clipped":[225],"but":[226],"cannot":[228],"tolerated.":[230]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":8},{"year":2024,"cited_by_count":5},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":5},{"year":2021,"cited_by_count":6},{"year":2020,"cited_by_count":7},{"year":2019,"cited_by_count":3},{"year":2018,"cited_by_count":4},{"year":2017,"cited_by_count":8},{"year":2016,"cited_by_count":2}],"updated_date":"2026-08-18T07:49:30.821534","created_date":"2025-10-10T00:00:00"}