{"id":"https://openalex.org/W1984232492","doi":"https://doi.org/10.1109/mlsp.2013.6661896","title":"Blind separation of spatially-block-sparse sources from orthogonal mixtures","display_name":"Blind separation of spatially-block-sparse sources from orthogonal mixtures","publication_year":2013,"publication_date":"2013-09-01","ids":{"openalex":"https://openalex.org/W1984232492","doi":"https://doi.org/10.1109/mlsp.2013.6661896","mag":"1984232492"},"language":"en","primary_location":{"id":"doi:10.1109/mlsp.2013.6661896","is_oa":false,"landing_page_url":"https://doi.org/10.1109/mlsp.2013.6661896","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2013 IEEE International Workshop on Machine Learning for Signal Processing (MLSP)","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/A5053239679","display_name":"Ofir Lindenbaum","orcid":"https://orcid.org/0000-0002-5990-742X"},"institutions":[{"id":"https://openalex.org/I16391192","display_name":"Tel Aviv University","ror":"https://ror.org/04mhzgx49","country_code":"IL","type":"education","lineage":["https://openalex.org/I16391192"]}],"countries":["IL"],"is_corresponding":false,"raw_author_name":"Ofir Lindenbaum","raw_affiliation_strings":["School of Electrical Engineering, Tel-Aviv University, Tel-Aviv, Israel","Sch. of Electr. Eng., Tel-Aviv Univ., Tel-Aviv, Israel"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electrical Engineering, Tel-Aviv University, Tel-Aviv, Israel","institution_ids":["https://openalex.org/I16391192"]},{"raw_affiliation_string":"Sch. of Electr. Eng., Tel-Aviv Univ., Tel-Aviv, Israel","institution_ids":["https://openalex.org/I16391192"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5011311476","display_name":"Arie Yeredor","orcid":"https://orcid.org/0000-0003-4296-6850"},"institutions":[{"id":"https://openalex.org/I16391192","display_name":"Tel Aviv University","ror":"https://ror.org/04mhzgx49","country_code":"IL","type":"education","lineage":["https://openalex.org/I16391192"]}],"countries":["IL"],"is_corresponding":false,"raw_author_name":"Arie Yeredor","raw_affiliation_strings":["School of Electrical Engineering, Tel-Aviv University, Tel-Aviv, Israel","Sch. of Electr. Eng., Tel-Aviv Univ., Tel-Aviv, Israel"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electrical Engineering, Tel-Aviv University, Tel-Aviv, Israel","institution_ids":["https://openalex.org/I16391192"]},{"raw_affiliation_string":"Sch. of Electr. Eng., Tel-Aviv Univ., Tel-Aviv, Israel","institution_ids":["https://openalex.org/I16391192"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5019397707","display_name":"Ran Vitek","orcid":null},"institutions":[{"id":"https://openalex.org/I1315810332","display_name":"Altair Engineering (United States)","ror":"https://ror.org/05939ef94","country_code":"US","type":"company","lineage":["https://openalex.org/I1315810332"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ran Vitek","raw_affiliation_strings":["Altair Hod Hasharon, Israel","Altair, Hod HaSharon, Israel"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Altair Hod Hasharon, Israel","institution_ids":[]},{"raw_affiliation_string":"Altair, Hod HaSharon, Israel","institution_ids":["https://openalex.org/I1315810332"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5013824899","display_name":"Moshe Mishali","orcid":"https://orcid.org/0000-0001-8735-2129"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Moshe Mishali","raw_affiliation_strings":["EZchip Yokneam, Israel","EZchip, Yokneam, Israel"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"EZchip Yokneam, Israel","institution_ids":[]},{"raw_affiliation_string":"EZchip, Yokneam, Israel","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.0637618,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"6","issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11447","display_name":"Blind Source Separation Techniques","score":1.0,"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"}},"topics":[{"id":"https://openalex.org/T11447","display_name":"Blind Source Separation Techniques","score":1.0,"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/T10640","display_name":"Spectroscopy and Chemometric Analyses","score":0.9948999881744385,"subfield":{"id":"https://openalex.org/subfields/1602","display_name":"Analytical Chemistry"},"field":{"id":"https://openalex.org/fields/16","display_name":"Chemistry"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10860","display_name":"Speech and Audio Processing","score":0.9857000112533569,"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/blind-signal-separation","display_name":"Blind signal separation","score":0.7137769460678101},{"id":"https://openalex.org/keywords/separation","display_name":"Separation (statistics)","score":0.6122281551361084},{"id":"https://openalex.org/keywords/block","display_name":"Block (permutation group theory)","score":0.6114141345024109},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4700293242931366},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3405505418777466},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.26835739612579346},{"id":"https://openalex.org/keywords/combinatorics","display_name":"Combinatorics","score":0.18979516625404358},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.10898974537849426},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.07202684879302979}],"concepts":[{"id":"https://openalex.org/C120317606","wikidata":"https://www.wikidata.org/wiki/Q17105967","display_name":"Blind signal separation","level":3,"score":0.7137769460678101},{"id":"https://openalex.org/C2776061190","wikidata":"https://www.wikidata.org/wiki/Q7451805","display_name":"Separation (statistics)","level":2,"score":0.6122281551361084},{"id":"https://openalex.org/C2777210771","wikidata":"https://www.wikidata.org/wiki/Q4927124","display_name":"Block (permutation group theory)","level":2,"score":0.6114141345024109},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4700293242931366},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3405505418777466},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.26835739612579346},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.18979516625404358},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.10898974537849426},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.07202684879302979},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/mlsp.2013.6661896","is_oa":false,"landing_page_url":"https://doi.org/10.1109/mlsp.2013.6661896","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2013 IEEE International Workshop on Machine Learning for Signal Processing (MLSP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.44999998807907104,"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":13,"referenced_works":["https://openalex.org/W1563800729","https://openalex.org/W2033419225","https://openalex.org/W2038237443","https://openalex.org/W2098996169","https://openalex.org/W2105454037","https://openalex.org/W2111136894","https://openalex.org/W2114595593","https://openalex.org/W2120696170","https://openalex.org/W2124757684","https://openalex.org/W2137335629","https://openalex.org/W2148527554","https://openalex.org/W2155981690","https://openalex.org/W6633621281"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2748952813","https://openalex.org/W2051487156","https://openalex.org/W2383482627","https://openalex.org/W2073681303","https://openalex.org/W2392054573","https://openalex.org/W2162758065","https://openalex.org/W2390279801","https://openalex.org/W2358668433","https://openalex.org/W2997589526"],"abstract_inverted_index":{"We":[0,181,196],"addresses":[1],"the":[2,25,63,66,86,91,103,107,114,129,132,135,150,154,164,176,183,186,200,203],"classical":[3],"problem":[4,47],"of":[5,8,39,57,65,90,110,113,134,145,206],"blind":[6],"separation":[7,14,209],"a":[9,69,216],"static":[10],"linear":[11],"mixture,":[12],"where":[13],"is":[15,82,96,102,140,160],"not":[16],"based":[17],"on":[18,29],"statistical":[19],"assumptions":[20],"(such":[21],"as":[22],"independence)":[23],"regarding":[24],"sources,":[26,67],"but":[27],"rather":[28],"their":[30,74],"spatial":[31,87],"(block-)":[32],"sparsity,":[33],"and":[34,53,55,68,98,131,148,193],"with":[35,116],"an":[36,40,83],"additional":[37],"constraint":[38],"orthogonal":[41],"mixing-matrix.":[42],"An":[43],"algorithm":[44,77,188,201],"for":[45,61,72,127,153,211,219],"this":[46,120],"was":[48],"recently":[49],"proposed":[50],"by":[51,170],"Mishali":[52],"Eldar,":[54],"consists":[56],"two":[58,79,124],"steps:":[59],"one":[60,71],"recovering":[62,73,163],"support":[64],"subsequent":[70],"values.":[75],"That":[76],"has":[78],"shortcomings:":[80],"One":[81],"assumption":[84],"that":[85,199],"sparsity":[88,165],"level":[89,166],"sources":[92],"at":[93,142,162,167,178],"each":[94,168],"time-instant":[95,169],"constant":[97],"known;":[99],"The":[100],"second":[101,158],"algorithm's":[104],"sensitivity":[105],"to":[106],"possible":[108],"presence":[109],"temporal":[111],"\u201cblocks\u201d":[112,144],"signals":[115,177],"identical":[117],"support.":[118],"In":[119],"work":[121],"we":[122],"propose":[123],"pre-processing":[125],"stages":[126],"improving":[128],"applicability":[130],"performance":[133],"algorithm.":[136],"A":[137,157],"first":[138],"stage":[139,159],"aimed":[141,161],"identifying":[143],"similar":[146],"support,":[147],"pruning":[149],"data":[151,192],"accordingly":[152],"support-recovery":[155],"stage.":[156],"exploiting":[171],"observed":[172],"structural":[173],"inter-relations":[174],"between":[175],"different":[179],"time-instants.":[180],"demonstrate":[182],"improvement":[184],"over":[185],"original":[187],"using":[189],"both":[190],"synthetic":[191],"mixed":[194],"text-images.":[195],"also":[197],"show":[198],"outperforms":[202],"recovery":[204],"rate":[205],"alternative":[207],"source":[208],"methods":[210],"such":[212],"contexts,":[213],"including":[214],"K-SVD,":[215],"leading":[217],"method":[218],"dictionary":[220],"learning.":[221]},"counts_by_year":[],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
