{"id":"https://openalex.org/W2133132487","doi":"https://doi.org/10.1109/icassp.2008.4518410","title":"Fourier and filterbank analyses of signal-dependent noise","display_name":"Fourier and filterbank analyses of signal-dependent noise","publication_year":2008,"publication_date":"2008-03-01","ids":{"openalex":"https://openalex.org/W2133132487","doi":"https://doi.org/10.1109/icassp.2008.4518410","mag":"2133132487"},"language":"en","primary_location":{"id":"doi:10.1109/icassp.2008.4518410","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp.2008.4518410","pdf_url":null,"source":{"id":"https://openalex.org/S4363607879","display_name":"IEEE International Conference on Acoustics Speech and Signal Processing","issn_l":"1520-6149","issn":["1520-6149","2379-190X"],"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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2008 IEEE International Conference on Acoustics, Speech and Signal Processing","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5084211411","display_name":"Keigo Hirakawa","orcid":"https://orcid.org/0000-0002-0818-7688"},"institutions":[{"id":"https://openalex.org/I136199984","display_name":"Harvard University","ror":"https://ror.org/03vek6s52","country_code":"US","type":"education","lineage":["https://openalex.org/I136199984"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Keigo Hirakawa","raw_affiliation_strings":["Department of Statistics, Harvard University, Cambridge, MA, USA","Dept. of Stat. One Oxford Street, Harvard Univ., Cambridge, MA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Statistics, Harvard University, Cambridge, MA, USA","institution_ids":["https://openalex.org/I136199984"]},{"raw_affiliation_string":"Dept. of Stat. One Oxford Street, Harvard Univ., Cambridge, MA","institution_ids":["https://openalex.org/I136199984"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5084211411"],"corresponding_institution_ids":["https://openalex.org/I136199984"],"apc_list":null,"apc_paid":null,"fwci":1.0427,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.76808633,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"3517","last_page":"3520"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10688","display_name":"Image and Signal Denoising Methods","score":0.9995999932289124,"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":0.9995999932289124,"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/T10928","display_name":"Probabilistic and Robust Engineering Design","score":0.9871000051498413,"subfield":{"id":"https://openalex.org/subfields/1804","display_name":"Statistics, Probability and Uncertainty"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T12300","display_name":"Advanced Electrical Measurement Techniques","score":0.984499990940094,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/spectral-density-estimation","display_name":"Spectral density estimation","score":0.601203978061676},{"id":"https://openalex.org/keywords/filter-bank","display_name":"Filter bank","score":0.5932015180587769},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.5456495881080627},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5013096332550049},{"id":"https://openalex.org/keywords/signal","display_name":"SIGNAL (programming language)","score":0.4868104159832001},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.48037630319595337},{"id":"https://openalex.org/keywords/fourier-transform","display_name":"Fourier transform","score":0.4621243476867676},{"id":"https://openalex.org/keywords/discrete-fourier-transform","display_name":"Discrete Fourier transform (general)","score":0.461503803730011},{"id":"https://openalex.org/keywords/noise-measurement","display_name":"Noise measurement","score":0.44315630197525024},{"id":"https://openalex.org/keywords/wavelet-transform","display_name":"Wavelet transform","score":0.4402488172054291},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.40394681692123413},{"id":"https://openalex.org/keywords/fourier-analysis","display_name":"Fourier analysis","score":0.3988915681838989},{"id":"https://openalex.org/keywords/wavelet","display_name":"Wavelet","score":0.3684318959712982},{"id":"https://openalex.org/keywords/short-time-fourier-transform","display_name":"Short-time Fourier transform","score":0.36707907915115356},{"id":"https://openalex.org/keywords/filter","display_name":"Filter (signal processing)","score":0.2391575276851654},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.21994361281394958},{"id":"https://openalex.org/keywords/noise-reduction","display_name":"Noise reduction","score":0.15720334649085999},{"id":"https://openalex.org/keywords/mathematical-analysis","display_name":"Mathematical analysis","score":0.15568512678146362},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.12917178869247437}],"concepts":[{"id":"https://openalex.org/C30049272","wikidata":"https://www.wikidata.org/wiki/Q6555326","display_name":"Spectral density estimation","level":3,"score":0.601203978061676},{"id":"https://openalex.org/C100515483","wikidata":"https://www.wikidata.org/wiki/Q3268235","display_name":"Filter bank","level":3,"score":0.5932015180587769},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.5456495881080627},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5013096332550049},{"id":"https://openalex.org/C2779843651","wikidata":"https://www.wikidata.org/wiki/Q7390335","display_name":"SIGNAL (programming language)","level":2,"score":0.4868104159832001},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.48037630319595337},{"id":"https://openalex.org/C102519508","wikidata":"https://www.wikidata.org/wiki/Q6520159","display_name":"Fourier transform","level":2,"score":0.4621243476867676},{"id":"https://openalex.org/C57733114","wikidata":"https://www.wikidata.org/wiki/Q1006032","display_name":"Discrete Fourier transform (general)","level":5,"score":0.461503803730011},{"id":"https://openalex.org/C29265498","wikidata":"https://www.wikidata.org/wiki/Q7047719","display_name":"Noise measurement","level":3,"score":0.44315630197525024},{"id":"https://openalex.org/C196216189","wikidata":"https://www.wikidata.org/wiki/Q2867","display_name":"Wavelet transform","level":3,"score":0.4402488172054291},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.40394681692123413},{"id":"https://openalex.org/C203024314","wikidata":"https://www.wikidata.org/wiki/Q1365258","display_name":"Fourier analysis","level":3,"score":0.3988915681838989},{"id":"https://openalex.org/C47432892","wikidata":"https://www.wikidata.org/wiki/Q831390","display_name":"Wavelet","level":2,"score":0.3684318959712982},{"id":"https://openalex.org/C166386157","wikidata":"https://www.wikidata.org/wiki/Q1477735","display_name":"Short-time Fourier transform","level":4,"score":0.36707907915115356},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.2391575276851654},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.21994361281394958},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.15720334649085999},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.15568512678146362},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.12917178869247437},{"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/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/icassp.2008.4518410","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp.2008.4518410","pdf_url":null,"source":{"id":"https://openalex.org/S4363607879","display_name":"IEEE International Conference on Acoustics Speech and Signal Processing","issn_l":"1520-6149","issn":["1520-6149","2379-190X"],"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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2008 IEEE International Conference on Acoustics, Speech and Signal Processing","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.219.6697","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.219.6697","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.accidentalmark.com/research/papers/Hirakawa08SigDep_ICASSP.pdf","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":27,"referenced_works":["https://openalex.org/W57051178","https://openalex.org/W755141289","https://openalex.org/W1571999879","https://openalex.org/W1598266570","https://openalex.org/W1766888123","https://openalex.org/W2023922195","https://openalex.org/W2048119506","https://openalex.org/W2054203776","https://openalex.org/W2054640142","https://openalex.org/W2059499875","https://openalex.org/W2072246452","https://openalex.org/W2079724595","https://openalex.org/W2087070363","https://openalex.org/W2091696547","https://openalex.org/W2093919938","https://openalex.org/W2108591694","https://openalex.org/W2125527601","https://openalex.org/W2130958226","https://openalex.org/W2134929491","https://openalex.org/W2136910298","https://openalex.org/W2153328975","https://openalex.org/W2161027669","https://openalex.org/W2170885533","https://openalex.org/W2256578114","https://openalex.org/W4255521522","https://openalex.org/W6635777585","https://openalex.org/W6682285136"],"related_works":["https://openalex.org/W2002136255","https://openalex.org/W2144045625","https://openalex.org/W3018445606","https://openalex.org/W2149419755","https://openalex.org/W4232826315","https://openalex.org/W2391648882","https://openalex.org/W4389480384","https://openalex.org/W2488177955","https://openalex.org/W2105558903","https://openalex.org/W3166675296"],"abstract_inverted_index":{"Owing":[0],"to":[1,73,102],"the":[2,7,10,14,17,20,27,47,89,104,108],"lack":[3],"of":[4,6,30,42,49,67,86,95,99],"resolution":[5],"measurement":[8,21],"and":[9,16,33,56,81,111],"randomness":[11],"inherent":[12],"in":[13,77,88,107],"signal":[15,43,106],"measuring":[18],"devices,":[19],"noise":[22,51,69,87],"is":[23,44],"often":[24],"signal-dependent.":[25],"Although":[26],"statistical":[28],"modeling":[29],"filterbank,":[31],"wavelets,":[32],"short-time":[34],"Fourier":[35,82],"coefficients":[36,55],"enjoys":[37],"immense":[38],"popularity,":[39],"transform-based":[40],"estimation":[41],"difficult":[45],"because":[46],"effects":[48],"signal-dependent":[50,68],"permeate":[52],"across":[53],"multiple":[54],"subbands.":[57],"In":[58],"this":[59],"work,":[60],"we":[61],"show":[62],"how":[63],"a":[64,78,93],"general":[65],"class":[66],"can":[70],"be":[71],"characterized":[72],"an":[74],"arbitrary":[75],"precision":[76],"Haar":[79],"filterbank":[80],"representation.":[83],"The":[84],"structure":[85],"transform":[90,109],"domain":[91],"admits":[92],"variant":[94],"Stein's":[96],"unbiased":[97],"estimate":[98],"risk":[100],"conducive":[101],"processing":[103],"corrupted":[105],"domain,":[110],"estimators":[112],"involving":[113],"Poisson":[114],"processes":[115],"are":[116],"discussed.":[117]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2017,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
