{"id":"https://openalex.org/W2810902605","doi":"https://doi.org/10.1109/isspit.2017.8388309","title":"Local linear predictive coding for high resolution time-frequency analysis","display_name":"Local linear predictive coding for high resolution time-frequency analysis","publication_year":2017,"publication_date":"2017-12-01","ids":{"openalex":"https://openalex.org/W2810902605","doi":"https://doi.org/10.1109/isspit.2017.8388309","mag":"2810902605"},"language":"en","primary_location":{"id":"doi:10.1109/isspit.2017.8388309","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isspit.2017.8388309","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT)","raw_type":"proceedings-article"},"type":"article","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/A5083895168","display_name":"Fumihiko Ishiyama","orcid":"https://orcid.org/0000-0002-1375-494X"},"institutions":[{"id":"https://openalex.org/I2251713219","display_name":"NTT (Japan)","ror":"https://ror.org/00berct97","country_code":"JP","type":"company","lineage":["https://openalex.org/I2251713219"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Fumihiko Ishiyama","raw_affiliation_strings":["NTT Network Technology Labs., Nippon Telegraph and Telephone Corp., Tokyo, Japan"],"affiliations":[{"raw_affiliation_string":"NTT Network Technology Labs., Nippon Telegraph and Telephone Corp., Tokyo, Japan","institution_ids":["https://openalex.org/I2251713219"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5083895168"],"corresponding_institution_ids":["https://openalex.org/I2251713219"],"apc_list":null,"apc_paid":null,"fwci":0.2731,"has_fulltext":false,"cited_by_count":10,"citation_normalized_percentile":{"value":0.66546046,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"001","last_page":"006"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10688","display_name":"Image and Signal Denoising Methods","score":0.9983999729156494,"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":0.9983999729156494,"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":0.9977999925613403,"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":0.9926000237464905,"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/time\u2013frequency-analysis","display_name":"Time\u2013frequency analysis","score":0.7197831273078918},{"id":"https://openalex.org/keywords/amplitude","display_name":"Amplitude","score":0.6323956847190857},{"id":"https://openalex.org/keywords/instantaneous-phase","display_name":"Instantaneous phase","score":0.585453450679779},{"id":"https://openalex.org/keywords/fourier-analysis","display_name":"Fourier analysis","score":0.5062288641929626},{"id":"https://openalex.org/keywords/fourier-transform","display_name":"Fourier transform","score":0.49612483382225037},{"id":"https://openalex.org/keywords/linear-predictive-coding","display_name":"Linear predictive coding","score":0.4927566349506378},{"id":"https://openalex.org/keywords/linear-prediction","display_name":"Linear prediction","score":0.4922691881656647},{"id":"https://openalex.org/keywords/resolution","display_name":"Resolution (logic)","score":0.4546821415424347},{"id":"https://openalex.org/keywords/frequency-analysis","display_name":"Frequency analysis","score":0.44363150000572205},{"id":"https://openalex.org/keywords/coding","display_name":"Coding (social sciences)","score":0.4331912398338318},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.40778404474258423},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.38917243480682373},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3854465186595917},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.26824188232421875},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.24156120419502258},{"id":"https://openalex.org/keywords/speech-processing","display_name":"Speech processing","score":0.17789620161056519},{"id":"https://openalex.org/keywords/mathematical-analysis","display_name":"Mathematical analysis","score":0.17752233147621155},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.16214823722839355},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.14803829789161682},{"id":"https://openalex.org/keywords/optics","display_name":"Optics","score":0.1356428861618042},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.12655094265937805}],"concepts":[{"id":"https://openalex.org/C142433447","wikidata":"https://www.wikidata.org/wiki/Q7806653","display_name":"Time\u2013frequency analysis","level":3,"score":0.7197831273078918},{"id":"https://openalex.org/C180205008","wikidata":"https://www.wikidata.org/wiki/Q159190","display_name":"Amplitude","level":2,"score":0.6323956847190857},{"id":"https://openalex.org/C137798554","wikidata":"https://www.wikidata.org/wiki/Q6038852","display_name":"Instantaneous phase","level":3,"score":0.585453450679779},{"id":"https://openalex.org/C203024314","wikidata":"https://www.wikidata.org/wiki/Q1365258","display_name":"Fourier analysis","level":3,"score":0.5062288641929626},{"id":"https://openalex.org/C102519508","wikidata":"https://www.wikidata.org/wiki/Q6520159","display_name":"Fourier transform","level":2,"score":0.49612483382225037},{"id":"https://openalex.org/C59883199","wikidata":"https://www.wikidata.org/wiki/Q1826438","display_name":"Linear predictive coding","level":3,"score":0.4927566349506378},{"id":"https://openalex.org/C131109320","wikidata":"https://www.wikidata.org/wiki/Q581012","display_name":"Linear prediction","level":2,"score":0.4922691881656647},{"id":"https://openalex.org/C138268822","wikidata":"https://www.wikidata.org/wiki/Q1051925","display_name":"Resolution (logic)","level":2,"score":0.4546821415424347},{"id":"https://openalex.org/C97118885","wikidata":"https://www.wikidata.org/wiki/Q565636","display_name":"Frequency analysis","level":2,"score":0.44363150000572205},{"id":"https://openalex.org/C179518139","wikidata":"https://www.wikidata.org/wiki/Q5140297","display_name":"Coding (social sciences)","level":2,"score":0.4331912398338318},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.40778404474258423},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.38917243480682373},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3854465186595917},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.26824188232421875},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.24156120419502258},{"id":"https://openalex.org/C61328038","wikidata":"https://www.wikidata.org/wiki/Q3358061","display_name":"Speech processing","level":2,"score":0.17789620161056519},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.17752233147621155},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.16214823722839355},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.14803829789161682},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.1356428861618042},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.12655094265937805},{"id":"https://openalex.org/C554190296","wikidata":"https://www.wikidata.org/wiki/Q47528","display_name":"Radar","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/isspit.2017.8388309","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isspit.2017.8388309","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":23,"referenced_works":["https://openalex.org/W1587896874","https://openalex.org/W1588317658","https://openalex.org/W1983662669","https://openalex.org/W1986754283","https://openalex.org/W1996021349","https://openalex.org/W1999762138","https://openalex.org/W2018763953","https://openalex.org/W2040532319","https://openalex.org/W2046133214","https://openalex.org/W2047121664","https://openalex.org/W2109069183","https://openalex.org/W2135691157","https://openalex.org/W2137089646","https://openalex.org/W2165878107","https://openalex.org/W2326591006","https://openalex.org/W2763392859","https://openalex.org/W2973818247","https://openalex.org/W3004157836","https://openalex.org/W3036278947","https://openalex.org/W3100950903","https://openalex.org/W4408097746","https://openalex.org/W6646433794","https://openalex.org/W6682747227"],"related_works":["https://openalex.org/W2363056088","https://openalex.org/W2365133740","https://openalex.org/W2134843605","https://openalex.org/W2438418151","https://openalex.org/W1973229893","https://openalex.org/W3091698330","https://openalex.org/W2808395304","https://openalex.org/W1619742598","https://openalex.org/W2362530701","https://openalex.org/W1978881573"],"abstract_inverted_index":{"We":[0,72],"are":[1],"developing":[2],"a":[3,41,63],"method":[4,29,75],"of":[5,11,26,44,60,62],"time-frequency":[6,70],"analysis":[7,52],"on":[8],"the":[9,58],"basis":[10],"linear":[12],"predictive":[13],"coding":[14],"(LPC).":[15],"In":[16],"contrast":[17],"to":[18,79,90],"standard":[19],"LPC,":[20],"which":[21],"assumes":[22],"an":[23],"infinite":[24],"number":[25,43],"samples,":[27],"our":[28,74],"obtains":[30],"instantaneous":[31,33,38],"frequencies,":[32],"amplitude":[34],"vary":[35],"rates,":[36],"and":[37,76],"amplitudes":[39],"from":[40],"small":[42],"samples.":[45],"The":[46],"time":[47,65,87],"width":[48],"for":[49],"single":[50],"local":[51],"can":[53],"be":[54],"much":[55],"narrower":[56],"than":[57],"period":[59],"oscillation":[61],"given":[64],"series,":[66],"enabling":[67],"high":[68],"resolution":[69],"analysis.":[71],"outline":[73],"use":[77],"it":[78],"analyze":[80],"electro-magnetic":[81],"noise":[82],"in":[83],"comparison":[84],"with":[85],"short":[86],"Fourier":[88],"transform":[89],"show":[91],"its":[92],"resolution.":[93]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":2},{"year":2018,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}