{"id":"https://openalex.org/W2063527931","doi":"https://doi.org/10.1109/icdsp.2013.6622682","title":"Decorrelating transforms for spectral vector quantization","display_name":"Decorrelating transforms for spectral vector quantization","publication_year":2013,"publication_date":"2013-07-01","ids":{"openalex":"https://openalex.org/W2063527931","doi":"https://doi.org/10.1109/icdsp.2013.6622682","mag":"2063527931"},"language":"en","primary_location":{"id":"doi:10.1109/icdsp.2013.6622682","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icdsp.2013.6622682","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2013 18th International Conference on Digital Signal Processing (DSP)","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/A5021252790","display_name":"Miguel Arjona Ram\u00edrez","orcid":"https://orcid.org/0000-0002-7107-0888"},"institutions":[{"id":"https://openalex.org/I17974374","display_name":"Universidade de S\u00e3o Paulo","ror":"https://ror.org/036rp1748","country_code":"BR","type":"education","lineage":["https://openalex.org/I17974374"]}],"countries":["BR"],"is_corresponding":true,"raw_author_name":"Miguel Arjona Ramirez","raw_affiliation_strings":["Department of Electronic Systems Engineering Escola Polit\u00e9cnica, University of S\u00e3o Paulo, Sao Paulo, Sao Paulo, Brazil","Dept. of Electron. Syst. Eng., Univ. of Sao Paulo, Sao Paulo, Brazil"],"affiliations":[{"raw_affiliation_string":"Department of Electronic Systems Engineering Escola Polit\u00e9cnica, University of S\u00e3o Paulo, Sao Paulo, Sao Paulo, Brazil","institution_ids":["https://openalex.org/I17974374"]},{"raw_affiliation_string":"Dept. of Electron. Syst. Eng., Univ. of Sao Paulo, Sao Paulo, Brazil","institution_ids":["https://openalex.org/I17974374"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5021252790"],"corresponding_institution_ids":["https://openalex.org/I17974374"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.12874009,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"1","issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10901","display_name":"Advanced Data Compression Techniques","score":0.9994999766349792,"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":0.9994999766349792,"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/T10688","display_name":"Image and Signal Denoising Methods","score":0.9941999912261963,"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":0.9908999800682068,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.6482415199279785},{"id":"https://openalex.org/keywords/karhunen\u2013lo\u00e8ve-theorem","display_name":"Karhunen\u2013Lo\u00e8ve theorem","score":0.6129150986671448},{"id":"https://openalex.org/keywords/vector-quantization","display_name":"Vector quantization","score":0.5986887216567993},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.5559433698654175},{"id":"https://openalex.org/keywords/quantization","display_name":"Quantization (signal processing)","score":0.4916902184486389},{"id":"https://openalex.org/keywords/transform-coding","display_name":"Transform coding","score":0.47827088832855225},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.3259080648422241},{"id":"https://openalex.org/keywords/discrete-cosine-transform","display_name":"Discrete cosine transform","score":0.3055449426174164},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.23731815814971924},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.08248013257980347}],"concepts":[{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.6482415199279785},{"id":"https://openalex.org/C109308471","wikidata":"https://www.wikidata.org/wiki/Q2046647","display_name":"Karhunen\u2013Lo\u00e8ve theorem","level":2,"score":0.6129150986671448},{"id":"https://openalex.org/C199833920","wikidata":"https://www.wikidata.org/wiki/Q612536","display_name":"Vector quantization","level":2,"score":0.5986887216567993},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.5559433698654175},{"id":"https://openalex.org/C28855332","wikidata":"https://www.wikidata.org/wiki/Q198099","display_name":"Quantization (signal processing)","level":2,"score":0.4916902184486389},{"id":"https://openalex.org/C169805256","wikidata":"https://www.wikidata.org/wiki/Q1361381","display_name":"Transform coding","level":4,"score":0.47827088832855225},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.3259080648422241},{"id":"https://openalex.org/C2221639","wikidata":"https://www.wikidata.org/wiki/Q2877","display_name":"Discrete cosine transform","level":3,"score":0.3055449426174164},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.23731815814971924},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.08248013257980347}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icdsp.2013.6622682","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icdsp.2013.6622682","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2013 18th International Conference on Digital Signal Processing (DSP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.4099999964237213,"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":16,"referenced_works":["https://openalex.org/W1989586763","https://openalex.org/W2002182716","https://openalex.org/W2048916503","https://openalex.org/W2079337129","https://openalex.org/W2100670758","https://openalex.org/W2122499135","https://openalex.org/W2124002824","https://openalex.org/W2152193917","https://openalex.org/W2156779572","https://openalex.org/W2157422526","https://openalex.org/W2165291881","https://openalex.org/W2167923766","https://openalex.org/W2168384618","https://openalex.org/W3127686677","https://openalex.org/W4301621763","https://openalex.org/W6789826613"],"related_works":["https://openalex.org/W2138957216","https://openalex.org/W2094583657","https://openalex.org/W3209251257","https://openalex.org/W1594300462","https://openalex.org/W2158009109","https://openalex.org/W1606460848","https://openalex.org/W1706111048","https://openalex.org/W2772846670","https://openalex.org/W2112852877","https://openalex.org/W2953035947"],"abstract_inverted_index":{"Split":[0],"vector":[1,41],"quantization":[2,141],"(SVQ)":[3],"performs":[4],"well":[5],"and":[6,60,103,175],"efficiently":[7],"for":[8,121,126],"line":[9],"spectral":[10],"frequency":[11],"(LSF)":[12],"quantization,":[13],"but":[14,58],"misses":[15],"some":[16,24],"component":[17,106,194],"dependencies.":[18],"Switched":[19],"SVQ":[20],"(SSVQ)":[21],"can":[22,43,138],"restore":[23],"advantage":[25,47],"due":[26,187],"to":[27,46,143,150,181,188],"nonlinear":[28],"dependencies":[29,37],"through":[30],"Gaussian":[31],"Mixture":[32],"Models":[33],"(GMM).":[34],"Remaining":[35],"linear":[36],"or":[38],"correlations":[39],"between":[40],"components":[42],"be":[44],"used":[45,57],"by":[48,77],"transform":[49,53,62,94,174],"coding.":[50],"The":[51,90],"Karhunen-Lo\u00e8ve":[52],"(KLT)":[54],"is":[55,96,104],"normally":[56],"eigendecomposition":[59],"full":[61],"matrices":[63],"make":[64],"it":[65],"computationally":[66],"complex.":[67],"However,":[68],"a":[69,105,117],"family":[70],"of":[71,80,85,100,110,112,163],"transforms":[72,102],"has":[73],"been":[74],"recently":[75],"characterized":[76],"the":[78,86,97,108,164,169,176,189,196],"capability":[79],"generalized":[81],"triangular":[82,93],"decomposition":[83,172],"(GTD)":[84],"source":[87],"covariance":[88],"matrix.":[89],"prediction-based":[91],"lower":[92],"(PLT)":[95],"least":[98],"complex":[99],"such":[101,167],"in":[107,195],"implementation":[109],"all":[111],"them.":[113],"This":[114],"paper":[115],"proposes":[116],"minimum":[118],"noise":[119],"structure":[120],"PLT":[122,136],"SVQ.":[123],"Coding":[124],"results":[125],"16-dimensional":[127],"LSF":[128],"vectors":[129],"from":[130],"wideband":[131],"speech":[132],"show":[133],"that":[134],"GMM":[135,151],"SSVQ":[137,153],"achieve":[139],"transparent":[140],"down":[142],"41":[144],"bit/frame":[145],"with":[146],"distortion":[147],"performance":[148],"close":[149],"KLT":[152],"at":[154],"about":[155],"three-fourths":[156],"as":[157,168],"much":[158],"operational":[159],"complexity.":[160],"Other":[161],"members":[162],"GTD":[165],"family,":[166],"geometric":[170],"mean":[171],"(GMD)":[173],"bidiagonal":[177],"(BID)":[178],"transform,":[179],"fail":[180],"capitalize":[182],"on":[183],"their":[184],"advantageous":[185],"features":[186],"low":[190],"bit":[191],"rate":[192],"per":[193],"range":[197],"tested.":[198]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
