{"id":"https://openalex.org/W2057935420","doi":"https://doi.org/10.1109/icassp.2014.6854951","title":"Exploiting long-term temporal dependencies in NMF using recurrent neural networks with application to source separation","display_name":"Exploiting long-term temporal dependencies in NMF using recurrent neural networks with application to source separation","publication_year":2014,"publication_date":"2014-05-01","ids":{"openalex":"https://openalex.org/W2057935420","doi":"https://doi.org/10.1109/icassp.2014.6854951","mag":"2057935420"},"language":"en","primary_location":{"id":"doi:10.1109/icassp.2014.6854951","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp.2014.6854951","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","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/A5037330846","display_name":"Nicolas Boulanger-Lewandowski","orcid":null},"institutions":[{"id":"https://openalex.org/I70931966","display_name":"Universit\u00e9 de Montr\u00e9al","ror":"https://ror.org/0161xgx34","country_code":"CA","type":"education","lineage":["https://openalex.org/I70931966"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Nicolas Boulanger-Lewandowski","raw_affiliation_strings":["Universit\u00e9 de Montr\u00e9al, Montr\u00e9al, QC, Canada","Univ. de Montreal Montreal, Montreal, QC, Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Universit\u00e9 de Montr\u00e9al, Montr\u00e9al, QC, Canada","institution_ids":["https://openalex.org/I70931966"]},{"raw_affiliation_string":"Univ. de Montreal Montreal, Montreal, QC, Canada","institution_ids":["https://openalex.org/I70931966"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5044614923","display_name":"Gautham J. Mysore","orcid":"https://orcid.org/0000-0003-0483-9252"},"institutions":[{"id":"https://openalex.org/I1306409833","display_name":"Adobe Systems (United States)","ror":"https://ror.org/059tvcg64","country_code":"US","type":"company","lineage":["https://openalex.org/I1306409833"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Gautham J. Mysore","raw_affiliation_strings":["Adobe Research, San Francisco, CA, USA","Adobe Res. San Francisco, San Francisco, CA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Adobe Research, San Francisco, CA, USA","institution_ids":["https://openalex.org/I1306409833"]},{"raw_affiliation_string":"Adobe Res. San Francisco, San Francisco, CA, USA","institution_ids":["https://openalex.org/I1306409833"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5109851187","display_name":"Matthew D. Hoffman","orcid":null},"institutions":[{"id":"https://openalex.org/I1306409833","display_name":"Adobe Systems (United States)","ror":"https://ror.org/059tvcg64","country_code":"US","type":"company","lineage":["https://openalex.org/I1306409833"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Matthew Hoffman","raw_affiliation_strings":["Adobe Research, San Francisco, CA, USA","Adobe Res. San Francisco, San Francisco, CA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Adobe Research, San Francisco, CA, USA","institution_ids":["https://openalex.org/I1306409833"]},{"raw_affiliation_string":"Adobe Res. San Francisco, San Francisco, CA, USA","institution_ids":["https://openalex.org/I1306409833"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":2.2361,"has_fulltext":false,"cited_by_count":17,"citation_normalized_percentile":{"value":0.89733629,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":"29","issue":null,"first_page":"6969","last_page":"6973"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10860","display_name":"Speech and Audio Processing","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/T10860","display_name":"Speech and Audio Processing","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/T11309","display_name":"Music and Audio Processing","score":0.9998999834060669,"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/T10201","display_name":"Speech Recognition and Synthesis","score":0.9994000196456909,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/non-negative-matrix-factorization","display_name":"Non-negative matrix factorization","score":0.8780542016029358},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8139554262161255},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.7126703858375549},{"id":"https://openalex.org/keywords/source-separation","display_name":"Source separation","score":0.6548235416412354},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.6249855756759644},{"id":"https://openalex.org/keywords/matrix-decomposition","display_name":"Matrix decomposition","score":0.618799090385437},{"id":"https://openalex.org/keywords/recurrent-neural-network","display_name":"Recurrent neural network","score":0.6053450703620911},{"id":"https://openalex.org/keywords/term","display_name":"Term (time)","score":0.5434548854827881},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.522029459476471},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.520070493221283},{"id":"https://openalex.org/keywords/event","display_name":"Event (particle physics)","score":0.48580169677734375},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4663761258125305},{"id":"https://openalex.org/keywords/temporal-database","display_name":"Temporal database","score":0.4610219895839691},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4030114710330963},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3654290437698364},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3533589243888855}],"concepts":[{"id":"https://openalex.org/C152671427","wikidata":"https://www.wikidata.org/wiki/Q10843505","display_name":"Non-negative matrix factorization","level":4,"score":0.8780542016029358},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8139554262161255},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.7126703858375549},{"id":"https://openalex.org/C2776864781","wikidata":"https://www.wikidata.org/wiki/Q52617913","display_name":"Source separation","level":2,"score":0.6548235416412354},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.6249855756759644},{"id":"https://openalex.org/C42355184","wikidata":"https://www.wikidata.org/wiki/Q1361088","display_name":"Matrix decomposition","level":3,"score":0.618799090385437},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.6053450703620911},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.5434548854827881},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.522029459476471},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.520070493221283},{"id":"https://openalex.org/C2779662365","wikidata":"https://www.wikidata.org/wiki/Q5416694","display_name":"Event (particle physics)","level":2,"score":0.48580169677734375},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4663761258125305},{"id":"https://openalex.org/C77277458","wikidata":"https://www.wikidata.org/wiki/Q1969246","display_name":"Temporal database","level":2,"score":0.4610219895839691},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4030114710330963},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3654290437698364},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3533589243888855},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C158693339","wikidata":"https://www.wikidata.org/wiki/Q190524","display_name":"Eigenvalues and eigenvectors","level":2,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/icassp.2014.6854951","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp.2014.6854951","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.704.104","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.704.104","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www-etud.iro.umontreal.ca/%7Eboulanni/ICASSP2014ss.pdf","raw_type":"text"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.907.5527","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.907.5527","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://mirlab.org/conference_papers/International_Conference/ICASSP%202014/papers/p7019-boulanger-lewandowski.pdf","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":43,"referenced_works":["https://openalex.org/W97505134","https://openalex.org/W104438260","https://openalex.org/W180176693","https://openalex.org/W1504438288","https://openalex.org/W1574263887","https://openalex.org/W1801637500","https://openalex.org/W1819710477","https://openalex.org/W1902027874","https://openalex.org/W1974774036","https://openalex.org/W1986227484","https://openalex.org/W2001987918","https://openalex.org/W2010757852","https://openalex.org/W2096482524","https://openalex.org/W2107878631","https://openalex.org/W2108563286","https://openalex.org/W2110096996","https://openalex.org/W2116064496","https://openalex.org/W2116216716","https://openalex.org/W2124914669","https://openalex.org/W2127851351","https://openalex.org/W2135029798","https://openalex.org/W2135104405","https://openalex.org/W2135341757","https://openalex.org/W2140574335","https://openalex.org/W2143612262","https://openalex.org/W2150415460","https://openalex.org/W2154642048","https://openalex.org/W2155531311","https://openalex.org/W2160485598","https://openalex.org/W2162911105","https://openalex.org/W2185726469","https://openalex.org/W2395935897","https://openalex.org/W2402209653","https://openalex.org/W2766736793","https://openalex.org/W2962968839","https://openalex.org/W3143596294","https://openalex.org/W6634388854","https://openalex.org/W6638211308","https://openalex.org/W6643914245","https://openalex.org/W6678801095","https://openalex.org/W6679825673","https://openalex.org/W6680012447","https://openalex.org/W6682610290"],"related_works":["https://openalex.org/W2037504162","https://openalex.org/W2774154397","https://openalex.org/W2146544734","https://openalex.org/W2921513691","https://openalex.org/W1979654135","https://openalex.org/W2156699640","https://openalex.org/W2098101267","https://openalex.org/W2081322759","https://openalex.org/W2156181515","https://openalex.org/W2158112352"],"abstract_inverted_index":{"This":[0,45],"paper":[1],"seeks":[2],"to":[3,19,34,51,81],"exploit":[4],"high-level":[5],"temporal":[6,25,37],"information":[7],"during":[8,60],"feature":[9,61],"extraction":[10,62],"from":[11,63],"audio":[12,65,75],"signals":[13],"via":[14],"non-negative":[15],"matrix":[16],"factorization.":[17],"Contrary":[18],"existing":[20],"approaches":[21],"that":[22],"impose":[23],"local":[24],"constraints,":[26],"we":[27],"train":[28],"powerful":[29],"recurrent":[30],"neural":[31],"network":[32],"models":[33],"capture":[35],"long-term":[36],"dependencies":[38],"and":[39],"event":[40],"co-occurrence":[41],"in":[42,53,56],"the":[43,49,54],"data.":[44],"gives":[46],"our":[47],"method":[48],"ability":[50,68],"\u201cfill":[52],"blanks\u201d":[55],"a":[57,72],"smart":[58],"way":[59],"complex":[64],"mixtures,":[66],"an":[67],"very":[69],"useful":[70],"for":[71],"number":[73],"of":[74],"applications.":[76],"We":[77],"apply":[78],"these":[79],"ideas":[80],"source":[82],"separation":[83],"problems.":[84]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":5},{"year":2017,"cited_by_count":2},{"year":2016,"cited_by_count":2},{"year":2015,"cited_by_count":2},{"year":2014,"cited_by_count":3}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
