{"id":"https://openalex.org/W2136837816","doi":"https://doi.org/10.21437/interspeech.2014-147","title":"Comparing time-frequency representations for directional derivative features","display_name":"Comparing time-frequency representations for directional derivative features","publication_year":2014,"publication_date":"2014-09-14","ids":{"openalex":"https://openalex.org/W2136837816","doi":"https://doi.org/10.21437/interspeech.2014-147","mag":"2136837816"},"language":"en","primary_location":{"id":"doi:10.21437/interspeech.2014-147","is_oa":false,"landing_page_url":"https://doi.org/10.21437/interspeech.2014-147","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Interspeech 2014","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/A5056048457","display_name":"James Gibson","orcid":"https://orcid.org/0000-0001-8572-307X"},"institutions":[{"id":"https://openalex.org/I1174212","display_name":"University of Southern California","ror":"https://ror.org/03taz7m60","country_code":"US","type":"education","lineage":["https://openalex.org/I1174212"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"James Gibson","raw_affiliation_strings":["University of Southern California, Los Angeles, United States"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Southern California, Los Angeles, United States","institution_ids":["https://openalex.org/I1174212"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5113701682","display_name":"Maarten Van Segbroeck","orcid":null},"institutions":[{"id":"https://openalex.org/I1174212","display_name":"University of Southern California","ror":"https://ror.org/03taz7m60","country_code":"US","type":"education","lineage":["https://openalex.org/I1174212"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Maarten Van Segbroeck","raw_affiliation_strings":["University of Southern California, Los Angeles, United States"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Southern California, Los Angeles, United States","institution_ids":["https://openalex.org/I1174212"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5010028928","display_name":"Shrikanth Narayanan","orcid":"https://orcid.org/0000-0002-1052-6204"},"institutions":[{"id":"https://openalex.org/I1174212","display_name":"University of Southern California","ror":"https://ror.org/03taz7m60","country_code":"US","type":"education","lineage":["https://openalex.org/I1174212"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Shrikanth S. Narayanan","raw_affiliation_strings":["University of Southern California, Los Angeles, United States"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Southern California, Los Angeles, United States","institution_ids":["https://openalex.org/I1174212"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.5897,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":{"value":0.69667218,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"612","last_page":"615"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10860","display_name":"Speech 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"}},"topics":[{"id":"https://openalex.org/T10860","display_name":"Speech 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":0.9993000030517578,"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/T11309","display_name":"Music and Audio Processing","score":0.9977999925613403,"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/spectrogram","display_name":"Spectrogram","score":0.9461896419525146},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7970157861709595},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.7818665504455566},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.7488334774971008},{"id":"https://openalex.org/keywords/filter-bank","display_name":"Filter bank","score":0.5446006059646606},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.5090846419334412},{"id":"https://openalex.org/keywords/time\u2013frequency-analysis","display_name":"Time\u2013frequency analysis","score":0.5030114054679871},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.49577197432518005},{"id":"https://openalex.org/keywords/dynamic-range-compression","display_name":"Dynamic range compression","score":0.482991099357605},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.42413291335105896},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4155729413032532},{"id":"https://openalex.org/keywords/short-time-fourier-transform","display_name":"Short-time Fourier transform","score":0.41299155354499817},{"id":"https://openalex.org/keywords/fourier-transform","display_name":"Fourier transform","score":0.3597383499145508},{"id":"https://openalex.org/keywords/filter","display_name":"Filter (signal processing)","score":0.3562738597393036},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.18847358226776123},{"id":"https://openalex.org/keywords/fourier-analysis","display_name":"Fourier analysis","score":0.1602535843849182},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1324758529663086},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.07612362504005432}],"concepts":[{"id":"https://openalex.org/C45273575","wikidata":"https://www.wikidata.org/wiki/Q578970","display_name":"Spectrogram","level":2,"score":0.9461896419525146},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7970157861709595},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.7818665504455566},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.7488334774971008},{"id":"https://openalex.org/C100515483","wikidata":"https://www.wikidata.org/wiki/Q3268235","display_name":"Filter bank","level":3,"score":0.5446006059646606},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.5090846419334412},{"id":"https://openalex.org/C142433447","wikidata":"https://www.wikidata.org/wiki/Q7806653","display_name":"Time\u2013frequency analysis","level":3,"score":0.5030114054679871},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.49577197432518005},{"id":"https://openalex.org/C150178126","wikidata":"https://www.wikidata.org/wiki/Q18433212","display_name":"Dynamic range compression","level":2,"score":0.482991099357605},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.42413291335105896},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4155729413032532},{"id":"https://openalex.org/C166386157","wikidata":"https://www.wikidata.org/wiki/Q1477735","display_name":"Short-time Fourier transform","level":4,"score":0.41299155354499817},{"id":"https://openalex.org/C102519508","wikidata":"https://www.wikidata.org/wiki/Q6520159","display_name":"Fourier transform","level":2,"score":0.3597383499145508},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.3562738597393036},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.18847358226776123},{"id":"https://openalex.org/C203024314","wikidata":"https://www.wikidata.org/wiki/Q1365258","display_name":"Fourier analysis","level":3,"score":0.1602535843849182},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1324758529663086},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.07612362504005432},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.21437/interspeech.2014-147","is_oa":false,"landing_page_url":"https://doi.org/10.21437/interspeech.2014-147","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Interspeech 2014","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":23,"referenced_works":["https://openalex.org/W9692546","https://openalex.org/W88081813","https://openalex.org/W1524333225","https://openalex.org/W1607274768","https://openalex.org/W1627087495","https://openalex.org/W1974387177","https://openalex.org/W1990934990","https://openalex.org/W2030937226","https://openalex.org/W2090861223","https://openalex.org/W2109812093","https://openalex.org/W2114719288","https://openalex.org/W2130426352","https://openalex.org/W2132606418","https://openalex.org/W2137075158","https://openalex.org/W2142709321","https://openalex.org/W2151484683","https://openalex.org/W2394487184","https://openalex.org/W2399455627","https://openalex.org/W2400523955","https://openalex.org/W2404548287","https://openalex.org/W2561557072","https://openalex.org/W2619993508","https://openalex.org/W3127686677"],"related_works":["https://openalex.org/W2120540196","https://openalex.org/W3095343173","https://openalex.org/W2288135719","https://openalex.org/W2323749021","https://openalex.org/W1889291648","https://openalex.org/W2533590149","https://openalex.org/W2381036744","https://openalex.org/W2901989338","https://openalex.org/W200102888","https://openalex.org/W4381416811"],"abstract_inverted_index":{"We":[0,38,64],"compare":[1],"the":[2,21,41,67,75,83,93,117],"performance":[3,84],"of":[4,44,50,69],"Directional":[5,113],"Derivatives":[6,114],"features":[7],"for":[8,58,106],"automatic":[9],"speech":[10,103],"recognition":[11,80,104],"when":[12],"extracted":[13,115],"from":[14,32,116],"different":[15],"time-frequency":[16],"representations.":[17],"Specifically,":[18],"we":[19,34],"use":[20],"short-time":[22],"Fourier":[23],"transform,":[24],"Mel-frequency,":[25],"and":[26,53,61,112],"Gammatone":[27],"spectrograms":[28],"as":[29],"a":[30],"base":[31],"which":[33],"extract":[35],"spectrotemporal":[36],"modulations.":[37],"then":[39],"assess":[40],"noise":[42],"robustness":[43],"each":[45],"representation":[46],"with":[47],"varied":[48],"number":[49],"frequency":[51],"bins":[52],"dynamic":[54,70],"range":[55,71],"compression":[56,72],"schemes":[57],"both":[59],"word":[60],"phone":[62],"recognition.":[63],"find":[65],"that":[66],"choice":[68],"approach":[73],"has":[74],"most":[76],"significant":[77,100],"impact":[78],"on":[79],"performance.":[81],"Whereas,":[82],"differences":[85],"between":[86],"perceptually":[87],"motivated":[88],"filter-banks":[89],"are":[90],"minimal":[91],"in":[92,102],"proposed":[94],"framework.":[95],"Furthermore,":[96],"this":[97],"work":[98],"presents":[99],"gains":[101],"accuracy":[105],"low":[107],"SNRs":[108],"over":[109],"MFCCs,":[110],"GFCCs,":[111],"log-Mel":[118],"spectrogram.":[119]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":2},{"year":2017,"cited_by_count":1},{"year":2016,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
