{"id":"https://openalex.org/W4402353859","doi":"https://doi.org/10.1109/ijcnn60899.2024.10651195","title":"Time-Invariant Latent Space Signatures for Enhanced Time-Frequency Domain Representation","display_name":"Time-Invariant Latent Space Signatures for Enhanced Time-Frequency Domain Representation","publication_year":2024,"publication_date":"2024-06-30","ids":{"openalex":"https://openalex.org/W4402353859","doi":"https://doi.org/10.1109/ijcnn60899.2024.10651195"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn60899.2024.10651195","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/ijcnn60899.2024.10651195","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 International Joint Conference on Neural Networks (IJCNN)","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":null,"display_name":"Chakka Sai Pradeep","orcid":null},"institutions":[{"id":"https://openalex.org/I181514455","display_name":"International Institute of Information Technology Bangalore","ror":"https://ror.org/05h9eqy10","country_code":"IN","type":"education","lineage":["https://openalex.org/I181514455"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Chakka Sai Pradeep","raw_affiliation_strings":["International Institute of Information Technology,Bangalore,India,560100"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"International Institute of Information Technology,Bangalore,India,560100","institution_ids":["https://openalex.org/I181514455"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5014371833","display_name":"Neelam Sinha","orcid":"https://orcid.org/0000-0001-8164-8412"},"institutions":[{"id":"https://openalex.org/I59270414","display_name":"Indian Institute of Science Bangalore","ror":"https://ror.org/04dese585","country_code":"IN","type":"education","lineage":["https://openalex.org/I59270414"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Neelam Sinha","raw_affiliation_strings":["Indian Institute of Science,Center for Brain Research,Bangalore,India,560012"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Indian Institute of Science,Center for Brain Research,Bangalore,India,560012","institution_ids":["https://openalex.org/I59270414"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.3336,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.782166,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":"355","issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11021","display_name":"ECG Monitoring and Analysis","score":0.9961000084877014,"subfield":{"id":"https://openalex.org/subfields/2705","display_name":"Cardiology and Cardiovascular Medicine"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},"topics":[{"id":"https://openalex.org/T11021","display_name":"ECG Monitoring and Analysis","score":0.9961000084877014,"subfield":{"id":"https://openalex.org/subfields/2705","display_name":"Cardiology and Cardiovascular Medicine"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T11447","display_name":"Blind Source Separation Techniques","score":0.9842000007629395,"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/T12419","display_name":"Phonocardiography and Auscultation Techniques","score":0.9715999960899353,"subfield":{"id":"https://openalex.org/subfields/2740","display_name":"Pulmonary and Respiratory Medicine"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/invariant","display_name":"Invariant (physics)","score":0.5791426301002502},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.5677397847175598},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4937160909175873},{"id":"https://openalex.org/keywords/frequency-domain","display_name":"Frequency domain","score":0.4874434769153595},{"id":"https://openalex.org/keywords/time\u2013frequency-analysis","display_name":"Time\u2013frequency analysis","score":0.48174428939819336},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.35411128401756287},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.13962486386299133}],"concepts":[{"id":"https://openalex.org/C190470478","wikidata":"https://www.wikidata.org/wiki/Q2370229","display_name":"Invariant (physics)","level":2,"score":0.5791426301002502},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.5677397847175598},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4937160909175873},{"id":"https://openalex.org/C19118579","wikidata":"https://www.wikidata.org/wiki/Q786423","display_name":"Frequency domain","level":2,"score":0.4874434769153595},{"id":"https://openalex.org/C142433447","wikidata":"https://www.wikidata.org/wiki/Q7806653","display_name":"Time\u2013frequency analysis","level":3,"score":0.48174428939819336},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.35411128401756287},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.13962486386299133},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C37914503","wikidata":"https://www.wikidata.org/wiki/Q156495","display_name":"Mathematical physics","level":1,"score":0.0},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn60899.2024.10651195","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/ijcnn60899.2024.10651195","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320310071","display_name":"Indian Institute of Science","ror":"https://ror.org/04dese585"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":31,"referenced_works":["https://openalex.org/W1644417361","https://openalex.org/W1757870343","https://openalex.org/W1924214379","https://openalex.org/W1963822842","https://openalex.org/W1972003923","https://openalex.org/W2045867791","https://openalex.org/W2047181473","https://openalex.org/W2077430201","https://openalex.org/W2164882523","https://openalex.org/W2333775360","https://openalex.org/W2482102801","https://openalex.org/W2748902594","https://openalex.org/W2785216882","https://openalex.org/W2798651459","https://openalex.org/W2913602408","https://openalex.org/W2913668833","https://openalex.org/W2950312697","https://openalex.org/W2967879902","https://openalex.org/W3080624533","https://openalex.org/W3106455851","https://openalex.org/W3189988478","https://openalex.org/W4287092031","https://openalex.org/W4287815523","https://openalex.org/W4289146965","https://openalex.org/W4372266952","https://openalex.org/W6640176828","https://openalex.org/W6675357634","https://openalex.org/W6748034048","https://openalex.org/W6771988894","https://openalex.org/W6782259796","https://openalex.org/W6793164127"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W1979597421","https://openalex.org/W2007980826","https://openalex.org/W2061531152","https://openalex.org/W3002753104","https://openalex.org/W1947057263","https://openalex.org/W2355447608","https://openalex.org/W1980067354","https://openalex.org/W1978333447","https://openalex.org/W2033324903"],"abstract_inverted_index":{"The":[0,44,121],"ability":[1],"to":[2],"analyze":[3],"data":[4],"is":[5,9,67,90],"enhanced,":[6],"when":[7],"it":[8],"represented":[10,68],"in":[11,81,107,193],"a":[12,28,70],"conducive":[13],"manner.":[14],"Here,":[15],"we":[16],"report":[17],"an":[18],"autoencoder-based":[19],"content-aware":[20],"2D":[21],"representation":[22,30,57,80,89],"of":[23,31,59,64,117,137,168,182],"1D":[24],"time-series.":[25,120],"We":[26,133],"propose":[27],"novel":[29],"time-series":[32,66,153,164],"utilizing":[33],"both":[34],"time-":[35,82],"and":[36,83,161],"frequency-domain":[37],"analyses,":[38],"carried":[39],"out":[40],"separately":[41],"but":[42],"concurrently.":[43],"loss":[45],"function":[46],"designed":[47],"for":[48,128,140],"autoencoder":[49],"training":[50],"ensures":[51],"that":[52,103,190],"the":[53,65,95,105,108,112,118,135,194],"learnt":[54],"latent":[55,78,96],"space":[56,79],"comprises":[58],"time-invariant":[60],"features.":[61],"Every":[62],"element":[63],"as":[69],"tuple":[71],"with":[72,173,185],"two":[73],"components,":[74],"one":[75],"each,":[76],"from":[77],"frequency-domains.":[84],"An":[85],"enhanced":[86],"time-frequency":[87],"domain":[88],"constructed":[91],"whose":[92],"axes":[93],"are":[94,151,178],"spaces.":[97],"In":[98],"this":[99],"representation,":[100],"those":[101,181],"tuples":[102],"represent":[104],"points":[106],"time-series,":[109],"together":[110],"form":[111],"\u201cLatent":[113],"Space":[114],"Signature\u201d":[115],"(LSS)":[116],"input":[119],"obtained":[122],"binary":[123],"LSS\u2019s":[124],"can":[125],"be":[126],"processed":[127],"classification,":[129,141],"saliency":[130],"determination,":[131],"etc.":[132],"illustrate":[134,191],"efficacy":[136],"LSS":[138],"technique":[139],"on":[142],"2":[143,174],"scenarios":[144],"(with":[145],"publicly":[146],"available,":[147],"bench-marked":[148],"datasets)":[149],"which":[150],"ECG":[152],"(109446":[154],"(5":[155],"labels)":[156],"+":[157],"14552":[158],"(2":[159],"labels))":[160],"Black":[162],"hole":[163],"(astronomy":[165],"data,":[166],"consisting":[167],"12":[169],"temporal":[170],"classes,":[171],"associated":[172],"labels).":[175],"Obtained":[176],"results":[177],"compared":[179],"against":[180],"popular":[183],"techniques,":[184],"concurring":[186],"or":[187],"improved":[188],"results,":[189],"promise":[192],"proposed":[195],"technique.":[196]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}