{"id":"https://openalex.org/W2972717745","doi":"https://doi.org/10.21437/interspeech.2019-2341","title":"Understanding and Visualizing Raw Waveform-Based CNNs","display_name":"Understanding and Visualizing Raw Waveform-Based CNNs","publication_year":2019,"publication_date":"2019-09-13","ids":{"openalex":"https://openalex.org/W2972717745","doi":"https://doi.org/10.21437/interspeech.2019-2341","mag":"2972717745"},"language":"en","primary_location":{"id":"doi:10.21437/interspeech.2019-2341","is_oa":false,"landing_page_url":"https://doi.org/10.21437/interspeech.2019-2341","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Interspeech 2019","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://publications.idiap.ch/attachments/reports/2018/Muckenhirn_Idiap-RR-11-2018.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5047091215","display_name":"Hannah Muckenhirn","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Hannah Muckenhirn","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5075520691","display_name":"Vinayak Abrol","orcid":"https://orcid.org/0000-0001-8149-8151"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Vinayak Abrol","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5043551083","display_name":"Mathew Magimai.-Doss","orcid":"https://orcid.org/0000-0002-8714-1409"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mathew Magimai-Doss","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5016330764","display_name":"S\u00e9bastien Marcel","orcid":"https://orcid.org/0000-0002-2497-9140"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"S\u00e9bastien Marcel","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5047091215"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":2.3803,"has_fulltext":true,"cited_by_count":28,"citation_normalized_percentile":{"value":0.91474053,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":96,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"2345","last_page":"2349"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10320","display_name":"Neural Networks and Applications","score":0.9958999752998352,"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"}},"topics":[{"id":"https://openalex.org/T10320","display_name":"Neural Networks and Applications","score":0.9958999752998352,"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"}},{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9932000041007996,"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"}},{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9821000099182129,"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/computer-science","display_name":"Computer science","score":0.6885045766830444},{"id":"https://openalex.org/keywords/waveform","display_name":"Waveform","score":0.6630057096481323},{"id":"https://openalex.org/keywords/computer-graphics","display_name":"Computer graphics (images)","score":0.3596689701080322},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3414424657821655},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.0968705415725708},{"id":"https://openalex.org/keywords/radar","display_name":"Radar","score":0.07034039497375488}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6885045766830444},{"id":"https://openalex.org/C197424946","wikidata":"https://www.wikidata.org/wiki/Q1165717","display_name":"Waveform","level":3,"score":0.6630057096481323},{"id":"https://openalex.org/C121684516","wikidata":"https://www.wikidata.org/wiki/Q7600677","display_name":"Computer graphics (images)","level":1,"score":0.3596689701080322},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3414424657821655},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0968705415725708},{"id":"https://openalex.org/C554190296","wikidata":"https://www.wikidata.org/wiki/Q47528","display_name":"Radar","level":2,"score":0.07034039497375488}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.21437/interspeech.2019-2341","is_oa":false,"landing_page_url":"https://doi.org/10.21437/interspeech.2019-2341","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Interspeech 2019","raw_type":"proceedings-article"},{"id":"pmh:oai:infoscience.epfl.ch:270134","is_oa":true,"landing_page_url":"http://infoscience.epfl.ch/record/270134","pdf_url":"https://publications.idiap.ch/attachments/reports/2018/Muckenhirn_Idiap-RR-11-2018.pdf","source":{"id":"https://openalex.org/S4306400487","display_name":"Infoscience (Ecole Polytechnique F\u00e9d\u00e9rale de Lausanne)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://infoscience.epfl.ch/record/270134","raw_type":"Text"}],"best_oa_location":{"id":"pmh:oai:infoscience.epfl.ch:270134","is_oa":true,"landing_page_url":"http://infoscience.epfl.ch/record/270134","pdf_url":"https://publications.idiap.ch/attachments/reports/2018/Muckenhirn_Idiap-RR-11-2018.pdf","source":{"id":"https://openalex.org/S4306400487","display_name":"Infoscience (Ecole Polytechnique F\u00e9d\u00e9rale de Lausanne)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://infoscience.epfl.ch/record/270134","raw_type":"Text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2972717745.pdf","grobid_xml":"https://content.openalex.org/works/W2972717745.grobid-xml"},"referenced_works_count":34,"referenced_works":["https://openalex.org/W9143300","https://openalex.org/W198601177","https://openalex.org/W1666984270","https://openalex.org/W1686810756","https://openalex.org/W1849277567","https://openalex.org/W1875231349","https://openalex.org/W1993882792","https://openalex.org/W2046056978","https://openalex.org/W2065234731","https://openalex.org/W2070696251","https://openalex.org/W2108598243","https://openalex.org/W2123045220","https://openalex.org/W2155273149","https://openalex.org/W2187585256","https://openalex.org/W2398826216","https://openalex.org/W2399733683","https://openalex.org/W2401869809","https://openalex.org/W2408093180","https://openalex.org/W2507877159","https://openalex.org/W2513345070","https://openalex.org/W2566781703","https://openalex.org/W2592641653","https://openalex.org/W2726515241","https://openalex.org/W2738359832","https://openalex.org/W2747238065","https://openalex.org/W2770454110","https://openalex.org/W2885329609","https://openalex.org/W2887949187","https://openalex.org/W2962851944","https://openalex.org/W2963175699","https://openalex.org/W2963382180","https://openalex.org/W2963828919","https://openalex.org/W4233131682","https://openalex.org/W4293861706"],"related_works":["https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W1974895211","https://openalex.org/W2176409448","https://openalex.org/W2129841057","https://openalex.org/W3040712279","https://openalex.org/W2364769705","https://openalex.org/W4367555392","https://openalex.org/W2374664672","https://openalex.org/W4246418678"],"abstract_inverted_index":{"Modeling":[0],"directly":[1],"raw":[2,124],"waveforms":[3],"through":[4,99],"neural":[5,31],"networks":[6,32],"for":[7,36,50,56],"speech":[8,41,59,83,101],"processing":[9,103],"is":[10,46],"gaining":[11],"more":[12,14],"and":[13,129],"attention.":[15],"Despite":[16],"its":[17],"varied":[18],"success,":[19],"a":[20,65,73],"question":[21],"that":[22,68,92],"remains":[23],"is:":[24],"what":[25],"kind":[26],"of":[27,81,94,118],"information":[28,108],"are":[29],"such":[30],"capturing":[33],"or":[34],"learning":[35],"different":[37],"tasks":[38],"from":[39],"the":[40,79,87,95,107,111,116,119],"signal?":[42],"Such":[43],"an":[44],"insight":[45],"not":[47],"only":[48],"interesting":[49],"advancing":[51],"those":[52],"techniques":[53,104],"but":[54],"also":[55],"understanding":[57],"better":[58],"signal":[60,102],"characteristics.":[61],"This":[62],"paper":[63],"takes":[64],"step":[66],"in":[67],"direction,":[69],"where":[70],"we":[71],"develop":[72],"gradient":[74],"based":[75],"approach":[76,121],"to":[77],"estimate":[78],"relevance":[80],"each":[82],"sample":[84],"input":[85],"on":[86],"output":[88],"score.":[89],"We":[90,114],"show":[91],"analysis":[93],"resulting":[96],"``relevance":[97],"signal\"":[98],"conventional":[100],"can":[105],"reveal":[106],"modeled":[109],"by":[110,122],"whole":[112],"network.":[113],"demonstrate":[115],"potential":[117],"proposed":[120],"analyzing":[123],"waveform":[125],"CNN-based":[126],"phone":[127],"recognition":[128],"speaker":[130],"identification":[131],"systems.":[132]},"counts_by_year":[{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":6},{"year":2021,"cited_by_count":7},{"year":2020,"cited_by_count":4}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
