{"id":"https://openalex.org/W1540083112","doi":"https://doi.org/10.1109/icassp.2015.7178904","title":"A multi-level representation of f0 using the continuous wavelet transform and the Discrete Cosine Transform","display_name":"A multi-level representation of f0 using the continuous wavelet transform and the Discrete Cosine Transform","publication_year":2015,"publication_date":"2015-04-01","ids":{"openalex":"https://openalex.org/W1540083112","doi":"https://doi.org/10.1109/icassp.2015.7178904","mag":"1540083112"},"language":"en","primary_location":{"id":"doi:10.1109/icassp.2015.7178904","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp.2015.7178904","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://www.pure.ed.ac.uk/ws/files/57836585/ribeiro_and_clark_icassp15.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5109494794","display_name":"Manuel Sam Ribeiro","orcid":"https://orcid.org/0000-0001-8096-2231"},"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":"Manuel Sam Ribeiro","raw_affiliation_strings":["Centre for Speech Technology Research, University of Edinburgh, UK","Centre for Speech Technology Research, University of Edinburgh,UK"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Centre for Speech Technology Research, University of Edinburgh, UK","institution_ids":["https://openalex.org/I98677209"]},{"raw_affiliation_string":"Centre for Speech Technology Research, University of Edinburgh,UK","institution_ids":["https://openalex.org/I98677209"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5084488961","display_name":"Robert A. Clark","orcid":"https://orcid.org/0000-0002-4892-3619"},"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":"Robert A. J. Clark","raw_affiliation_strings":["Centre for Speech Technology Research, University of Edinburgh, UK","Centre for Speech Technology Research, University of Edinburgh,UK"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Centre for Speech Technology Research, University of Edinburgh, UK","institution_ids":["https://openalex.org/I98677209"]},{"raw_affiliation_string":"Centre for Speech Technology Research, University of Edinburgh,UK","institution_ids":["https://openalex.org/I98677209"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":2.8082,"has_fulltext":true,"cited_by_count":33,"citation_normalized_percentile":{"value":0.93578132,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":"1","issue":null,"first_page":"4909","last_page":"4913"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10688","display_name":"Image and Signal Denoising Methods","score":0.9987000226974487,"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":0.9987000226974487,"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/T11447","display_name":"Blind Source Separation Techniques","score":0.9969000220298767,"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/T10220","display_name":"Machine Fault Diagnosis Techniques","score":0.9932000041007996,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems 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/discrete-cosine-transform","display_name":"Discrete cosine transform","score":0.9373300075531006},{"id":"https://openalex.org/keywords/modified-discrete-cosine-transform","display_name":"Modified discrete cosine transform","score":0.7185945510864258},{"id":"https://openalex.org/keywords/continuous-wavelet-transform","display_name":"Continuous wavelet transform","score":0.6332986354827881},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.6004759669303894},{"id":"https://openalex.org/keywords/lapped-transform","display_name":"Lapped transform","score":0.5429285764694214},{"id":"https://openalex.org/keywords/trigonometric-functions","display_name":"Trigonometric functions","score":0.5117995142936707},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.5099107027053833},{"id":"https://openalex.org/keywords/discrete-wavelet-transform","display_name":"Discrete wavelet transform","score":0.4843198359012604},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4759918451309204},{"id":"https://openalex.org/keywords/harmonic-wavelet-transform","display_name":"Harmonic wavelet transform","score":0.4544943869113922},{"id":"https://openalex.org/keywords/wavelet-transform","display_name":"Wavelet transform","score":0.45438823103904724},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.45189571380615234},{"id":"https://openalex.org/keywords/discrete-sine-transform","display_name":"Discrete sine transform","score":0.44509562849998474},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4383794367313385},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3978468179702759},{"id":"https://openalex.org/keywords/wavelet","display_name":"Wavelet","score":0.39458340406417847},{"id":"https://openalex.org/keywords/transform-coding","display_name":"Transform coding","score":0.38519081473350525},{"id":"https://openalex.org/keywords/fourier-transform","display_name":"Fourier transform","score":0.31830012798309326},{"id":"https://openalex.org/keywords/short-time-fourier-transform","display_name":"Short-time Fourier transform","score":0.2567085921764374},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.09835317730903625},{"id":"https://openalex.org/keywords/mathematical-analysis","display_name":"Mathematical analysis","score":0.07950863242149353},{"id":"https://openalex.org/keywords/fourier-analysis","display_name":"Fourier analysis","score":0.06703287363052368}],"concepts":[{"id":"https://openalex.org/C2221639","wikidata":"https://www.wikidata.org/wiki/Q2877","display_name":"Discrete cosine transform","level":3,"score":0.9373300075531006},{"id":"https://openalex.org/C28726691","wikidata":"https://www.wikidata.org/wiki/Q1268231","display_name":"Modified discrete cosine transform","level":5,"score":0.7185945510864258},{"id":"https://openalex.org/C95722684","wikidata":"https://www.wikidata.org/wiki/Q2622756","display_name":"Continuous wavelet transform","level":5,"score":0.6332986354827881},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.6004759669303894},{"id":"https://openalex.org/C91458471","wikidata":"https://www.wikidata.org/wiki/Q17096468","display_name":"Lapped transform","level":5,"score":0.5429285764694214},{"id":"https://openalex.org/C178009071","wikidata":"https://www.wikidata.org/wiki/Q93344","display_name":"Trigonometric functions","level":2,"score":0.5117995142936707},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.5099107027053833},{"id":"https://openalex.org/C46286280","wikidata":"https://www.wikidata.org/wiki/Q2414958","display_name":"Discrete wavelet transform","level":4,"score":0.4843198359012604},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4759918451309204},{"id":"https://openalex.org/C1109138","wikidata":"https://www.wikidata.org/wiki/Q3280930","display_name":"Harmonic wavelet transform","level":5,"score":0.4544943869113922},{"id":"https://openalex.org/C196216189","wikidata":"https://www.wikidata.org/wiki/Q2867","display_name":"Wavelet transform","level":3,"score":0.45438823103904724},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.45189571380615234},{"id":"https://openalex.org/C167058841","wikidata":"https://www.wikidata.org/wiki/Q971039","display_name":"Discrete sine transform","level":5,"score":0.44509562849998474},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4383794367313385},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3978468179702759},{"id":"https://openalex.org/C47432892","wikidata":"https://www.wikidata.org/wiki/Q831390","display_name":"Wavelet","level":2,"score":0.39458340406417847},{"id":"https://openalex.org/C169805256","wikidata":"https://www.wikidata.org/wiki/Q1361381","display_name":"Transform coding","level":4,"score":0.38519081473350525},{"id":"https://openalex.org/C102519508","wikidata":"https://www.wikidata.org/wiki/Q6520159","display_name":"Fourier transform","level":2,"score":0.31830012798309326},{"id":"https://openalex.org/C166386157","wikidata":"https://www.wikidata.org/wiki/Q1477735","display_name":"Short-time Fourier transform","level":4,"score":0.2567085921764374},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.09835317730903625},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.07950863242149353},{"id":"https://openalex.org/C203024314","wikidata":"https://www.wikidata.org/wiki/Q1365258","display_name":"Fourier analysis","level":3,"score":0.06703287363052368},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/icassp.2015.7178904","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp.2015.7178904","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-article"},{"id":"pmh:oai:pure.ed.ac.uk:publications/71055158-debe-4bbd-9c62-f91f87fce0d3","is_oa":true,"landing_page_url":"https://www.research.ed.ac.uk/en/publications/71055158-debe-4bbd-9c62-f91f87fce0d3","pdf_url":"https://www.pure.ed.ac.uk/ws/files/57836585/ribeiro_and_clark_icassp15.pdf","source":{"id":"https://openalex.org/S4406922455","display_name":"Edinburgh Research Explorer","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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":""},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.714.8513","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.714.8513","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.cstr.ed.ac.uk/downloads/publications/2015/ribeiro_and_clark_icassp15.pdf","raw_type":"text"}],"best_oa_location":{"id":"pmh:oai:pure.ed.ac.uk:publications/71055158-debe-4bbd-9c62-f91f87fce0d3","is_oa":true,"landing_page_url":"https://www.research.ed.ac.uk/en/publications/71055158-debe-4bbd-9c62-f91f87fce0d3","pdf_url":"https://www.pure.ed.ac.uk/ws/files/57836585/ribeiro_and_clark_icassp15.pdf","source":{"id":"https://openalex.org/S4406922455","display_name":"Edinburgh Research Explorer","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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":""},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W1540083112.pdf","grobid_xml":"https://content.openalex.org/works/W1540083112.grobid-xml"},"referenced_works_count":26,"referenced_works":["https://openalex.org/W36638734","https://openalex.org/W113106864","https://openalex.org/W251642908","https://openalex.org/W397522103","https://openalex.org/W1512429158","https://openalex.org/W1517939602","https://openalex.org/W1961629950","https://openalex.org/W1998765649","https://openalex.org/W2029190953","https://openalex.org/W2029434926","https://openalex.org/W2106421426","https://openalex.org/W2109938215","https://openalex.org/W2111284386","https://openalex.org/W2116927898","https://openalex.org/W2127589467","https://openalex.org/W2158504378","https://openalex.org/W2195693064","https://openalex.org/W2296111744","https://openalex.org/W2330979245","https://openalex.org/W2406159955","https://openalex.org/W2911964244","https://openalex.org/W4388277895","https://openalex.org/W6601463856","https://openalex.org/W6604530167","https://openalex.org/W6630838124","https://openalex.org/W6678929712"],"related_works":["https://openalex.org/W2090071970","https://openalex.org/W2162505377","https://openalex.org/W2963134360","https://openalex.org/W2001126479","https://openalex.org/W2978563117","https://openalex.org/W2354377491","https://openalex.org/W2031614119","https://openalex.org/W2363607146","https://openalex.org/W4309177872","https://openalex.org/W2024146866"],"abstract_inverted_index":{"We":[0],"propose":[1],"a":[2,37],"representation":[3,71],"of":[4,26,40,49,109],"f0":[5,73],"using":[6,99],"the":[7,13,21,30,47,87,94],"Continuous":[8],"Wavelet":[9],"Transform":[10,16],"(CWT)":[11],"and":[12,53,62,90,106],"Discrete":[14],"Cosine":[15],"(DCT).":[17],"The":[18,43],"CWT":[19],"decomposes":[20],"signal":[22,51],"into":[23],"various":[24],"scales":[25],"selected":[27],"frequencies,":[28],"while":[29,98],"DCT":[31],"compactly":[32],"represents":[33],"complex":[34],"contours":[35],"as":[36],"weighted":[38],"sum":[39],"cosine":[41],"functions.":[42],"proposed":[44,96],"approach":[45],"has":[46],"advantage":[48],"combining":[50],"decomposition":[52],"higher-level":[54],"representations,":[55],"thus":[56],"modeling":[57],"low-frequencies":[58],"at":[59,64],"higher":[60],"levels":[61],"high-frequencies":[63],"lower-levels.":[65],"Objective":[66],"results":[67,80,103],"indicate":[68],"that":[69,82],"this":[70],"improves":[72],"prediction":[74],"over":[75,86],"traditional":[76],"short-term":[77],"approaches.":[78],"Subjective":[79],"show":[81],"improvements":[83],"are":[84,91,104,111],"seen":[85],"typical":[88],"MSD-HMM":[89],"comparable":[92],"to":[93],"recently":[95],"CWT-HMM,":[97],"less":[100],"parameters.":[101],"These":[102],"discussed":[105],"future":[107],"lines":[108],"research":[110],"proposed.":[112]},"counts_by_year":[{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":4},{"year":2020,"cited_by_count":4},{"year":2019,"cited_by_count":9},{"year":2018,"cited_by_count":3},{"year":2017,"cited_by_count":3},{"year":2016,"cited_by_count":7},{"year":2015,"cited_by_count":2}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}