{"id":"https://openalex.org/W2151626637","doi":"https://doi.org/10.1109/icassp.1985.1168147","title":"Code-excited linear prediction(CELP): High-quality speech at very low bit rates","display_name":"Code-excited linear prediction(CELP): High-quality speech at very low bit rates","publication_year":2005,"publication_date":"2005-03-23","ids":{"openalex":"https://openalex.org/W2151626637","doi":"https://doi.org/10.1109/icassp.1985.1168147","mag":"2151626637"},"language":"en","primary_location":{"id":"doi:10.1109/icassp.1985.1168147","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp.1985.1168147","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP '85. IEEE International Conference on Acoustics, Speech, and Signal Processing","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/A5110096821","display_name":"Manfred R. Schroeder","orcid":null},"institutions":[{"id":"https://openalex.org/I74656192","display_name":"University of G\u00f6ttingen","ror":"https://ror.org/01y9bpm73","country_code":"DE","type":"education","lineage":["https://openalex.org/I74656192"]},{"id":"https://openalex.org/I1283103587","display_name":"AT&T (United States)","ror":"https://ror.org/02bbd5539","country_code":"US","type":"company","lineage":["https://openalex.org/I1283103587"]}],"countries":["DE","US"],"is_corresponding":true,"raw_author_name":"M. Schroeder","raw_affiliation_strings":["AT and T Bell Laboratories, Inc., Murray Hill, NJ, USA","Drittes Physikalisches Institut, University of Goettingen, Germany"],"affiliations":[{"raw_affiliation_string":"AT and T Bell Laboratories, Inc., Murray Hill, NJ, USA","institution_ids":["https://openalex.org/I1283103587"]},{"raw_affiliation_string":"Drittes Physikalisches Institut, University of Goettingen, Germany","institution_ids":["https://openalex.org/I74656192"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5012446143","display_name":"Bishnu S. Atal","orcid":"https://orcid.org/0000-0003-1455-6037"},"institutions":[{"id":"https://openalex.org/I1283103587","display_name":"AT&T (United States)","ror":"https://ror.org/02bbd5539","country_code":"US","type":"company","lineage":["https://openalex.org/I1283103587"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"B. Atal","raw_affiliation_strings":["AT and T Bell Laboratories, Inc., Murray Hill, NJ, USA"],"affiliations":[{"raw_affiliation_string":"AT and T Bell Laboratories, Inc., Murray Hill, NJ, USA","institution_ids":["https://openalex.org/I1283103587"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5110096821"],"corresponding_institution_ids":["https://openalex.org/I1283103587","https://openalex.org/I74656192"],"apc_list":null,"apc_paid":null,"fwci":47.5871,"has_fulltext":false,"cited_by_count":1049,"citation_normalized_percentile":{"value":0.99928843,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":100},"biblio":{"volume":"10","issue":null,"first_page":"937","last_page":"940"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10901","display_name":"Advanced Data Compression Techniques","score":0.9997000098228455,"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/T10901","display_name":"Advanced Data Compression Techniques","score":0.9997000098228455,"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/T10860","display_name":"Speech and Audio Processing","score":0.9957000017166138,"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":0.9912999868392944,"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/code-excited-linear-prediction","display_name":"Code-excited linear prediction","score":0.8804171085357666},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6755179762840271},{"id":"https://openalex.org/keywords/vector-sum-excited-linear-prediction","display_name":"Vector sum excited linear prediction","score":0.6062945127487183},{"id":"https://openalex.org/keywords/linear-predictive-coding","display_name":"Linear predictive coding","score":0.5773378014564514},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.5532867908477783},{"id":"https://openalex.org/keywords/code","display_name":"Code (set theory)","score":0.5418738722801208},{"id":"https://openalex.org/keywords/sequence","display_name":"Sequence (biology)","score":0.5386824607849121},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.5034405589103699},{"id":"https://openalex.org/keywords/linear-prediction","display_name":"Linear prediction","score":0.4721013605594635},{"id":"https://openalex.org/keywords/speech-coding","display_name":"Speech coding","score":0.47001269459724426}],"concepts":[{"id":"https://openalex.org/C105964291","wikidata":"https://www.wikidata.org/wiki/Q856184","display_name":"Code-excited linear prediction","level":4,"score":0.8804171085357666},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6755179762840271},{"id":"https://openalex.org/C138807605","wikidata":"https://www.wikidata.org/wiki/Q7917845","display_name":"Vector sum excited linear prediction","level":5,"score":0.6062945127487183},{"id":"https://openalex.org/C59883199","wikidata":"https://www.wikidata.org/wiki/Q1826438","display_name":"Linear predictive coding","level":3,"score":0.5773378014564514},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5532867908477783},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.5418738722801208},{"id":"https://openalex.org/C2778112365","wikidata":"https://www.wikidata.org/wiki/Q3511065","display_name":"Sequence (biology)","level":2,"score":0.5386824607849121},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.5034405589103699},{"id":"https://openalex.org/C131109320","wikidata":"https://www.wikidata.org/wiki/Q581012","display_name":"Linear prediction","level":2,"score":0.4721013605594635},{"id":"https://openalex.org/C13895895","wikidata":"https://www.wikidata.org/wiki/Q3270773","display_name":"Speech coding","level":2,"score":0.47001269459724426},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.0},{"id":"https://openalex.org/C54355233","wikidata":"https://www.wikidata.org/wiki/Q7162","display_name":"Genetics","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icassp.1985.1168147","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp.1985.1168147","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP '85. IEEE International Conference on Acoustics, Speech, and Signal Processing","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.6499999761581421,"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":4,"referenced_works":["https://openalex.org/W1936264760","https://openalex.org/W1970272279","https://openalex.org/W2050244349","https://openalex.org/W2099856211"],"related_works":["https://openalex.org/W1570840316","https://openalex.org/W1982751076","https://openalex.org/W2051489660","https://openalex.org/W2119342582","https://openalex.org/W2794974041","https://openalex.org/W2350749055","https://openalex.org/W1042315724","https://openalex.org/W2111369522","https://openalex.org/W2402025388","https://openalex.org/W2114934544"],"abstract_inverted_index":{"We":[0,74,114],"describe":[1],"in":[2,10,55,70,81,116,129,135],"this":[3,117],"paper":[4,118],"a":[5,19,27,48,63,142,147],"code-excited":[6],"linear":[7,43],"predictive":[8],"coder":[9],"which":[11],"the":[12,34,56,60,71,105,131,161,168],"optimum":[13,132],"innovation":[14,35,99,106,133],"sequence":[15,36,107,134],"is":[16,37,92,108],"selected":[17],"from":[18,94],"code":[20,75,124,144],"book":[21,145],"of":[22,33,83,89,96,157],"stored":[23],"sequences":[24],"to":[25,51,67],"optimize":[26],"given":[28],"fidelity":[29],"criterion.":[30],"Each":[31,86],"sample":[32],"filtered":[38],"sequentially":[39],"through":[40],"two":[41],"time-varying":[42],"recursive":[44],"filters,":[45],"one":[46,95],"with":[47,62],"long-delay":[49],"(related":[50,66],"pitch":[52],"period)":[53],"predictor":[54,65],"feedback":[57,72],"loop":[58],"and":[59,122],"other":[61],"short-delay":[64],"spectral":[68],"envelope)":[69],"loop.":[73],"speech,":[76],"sampled":[77],"at":[78,152,167],"8":[79],"kHz,":[80],"blocks":[82],"5-msec":[84],"duration.":[85],"block":[87],"consisting":[88],"40":[90],"samples":[91],"produced":[93,159],"1024":[97],"possible":[98],"sequences.":[100],"The":[101],"bit":[102,111,154],"rate":[103],"for":[104,126],"thus":[109],"1/4":[110],"per":[112],"sample.":[113],"compare":[115],"several":[119],"different":[120],"random":[121,143],"deterministic":[123],"books":[125],"their":[127],"effectiveness":[128],"providing":[130],"each":[136],"block.":[137],"Our":[138],"results":[139],"indicate":[140],"that":[141],"has":[146],"slight":[148],"speech":[149,158],"quality":[150],"advantage":[151],"low":[153],"rates.":[155],"Examples":[156],"by":[160],"above":[162],"method":[163],"will":[164],"be":[165],"played":[166],"conference.":[169]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":11},{"year":2024,"cited_by_count":9},{"year":2023,"cited_by_count":8},{"year":2022,"cited_by_count":7},{"year":2021,"cited_by_count":21},{"year":2020,"cited_by_count":16},{"year":2019,"cited_by_count":19},{"year":2018,"cited_by_count":12},{"year":2017,"cited_by_count":14},{"year":2016,"cited_by_count":23},{"year":2015,"cited_by_count":13},{"year":2014,"cited_by_count":23},{"year":2013,"cited_by_count":19},{"year":2012,"cited_by_count":14}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}