{"id":"https://openalex.org/W2514103081","doi":"https://doi.org/10.1109/ssp.2016.7551748","title":"Use of particle filtering and MCMC for inference in Probabilistic Acoustic Tube model","display_name":"Use of particle filtering and MCMC for inference in Probabilistic Acoustic Tube model","publication_year":2016,"publication_date":"2016-06-01","ids":{"openalex":"https://openalex.org/W2514103081","doi":"https://doi.org/10.1109/ssp.2016.7551748","mag":"2514103081"},"language":"en","primary_location":{"id":"doi:10.1109/ssp.2016.7551748","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ssp.2016.7551748","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE Statistical Signal Processing Workshop (SSP)","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/A5010338611","display_name":"Ruobai Wang","orcid":null},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ruobai Wang","raw_affiliation_strings":["Department of Electronic Engineering, Tsinghua University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electronic Engineering, Tsinghua University","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100354733","display_name":"Yang Zhang","orcid":"https://orcid.org/0000-0002-8540-1254"},"institutions":[{"id":"https://openalex.org/I157725225","display_name":"University of Illinois Urbana-Champaign","ror":"https://ror.org/047426m28","country_code":"US","type":"education","lineage":["https://openalex.org/I157725225"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yang Zhang","raw_affiliation_strings":["Urbana-Champaign, University of Illinois"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Urbana-Champaign, University of Illinois","institution_ids":["https://openalex.org/I157725225"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5010173604","display_name":"Zhijian Ou","orcid":"https://orcid.org/0000-0002-9018-5074"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhijian Ou","raw_affiliation_strings":["Department of Electronic Engineering, Tsinghua University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electronic Engineering, Tsinghua University","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5004778663","display_name":"Mark Hasegawa\u2010Johnson","orcid":"https://orcid.org/0000-0002-5631-2893"},"institutions":[{"id":"https://openalex.org/I157725225","display_name":"University of Illinois Urbana-Champaign","ror":"https://ror.org/047426m28","country_code":"US","type":"education","lineage":["https://openalex.org/I157725225"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Mark Hasegawa-Johnson","raw_affiliation_strings":["Urbana-Champaign, University of Illinois"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Urbana-Champaign, University of Illinois","institution_ids":["https://openalex.org/I157725225"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10860","display_name":"Speech and Audio Processing","score":0.9998000264167786,"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":0.9998000264167786,"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":0.9995999932289124,"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":0.9945999979972839,"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/particle-filter","display_name":"Particle filter","score":0.7936058044433594},{"id":"https://openalex.org/keywords/markov-chain-monte-carlo","display_name":"Markov chain Monte Carlo","score":0.6904928684234619},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6598051190376282},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.5667502284049988},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5576596856117249},{"id":"https://openalex.org/keywords/autoregressive-model","display_name":"Autoregressive model","score":0.556186318397522},{"id":"https://openalex.org/keywords/hidden-markov-model","display_name":"Hidden Markov model","score":0.5211705565452576},{"id":"https://openalex.org/keywords/generative-model","display_name":"Generative model","score":0.4646871089935303},{"id":"https://openalex.org/keywords/statistical-model","display_name":"Statistical model","score":0.4583207666873932},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.45226114988327026},{"id":"https://openalex.org/keywords/bayesian-inference","display_name":"Bayesian inference","score":0.44005823135375977},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.35219743847846985},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.33474212884902954},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.2518334686756134},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.22222933173179626},{"id":"https://openalex.org/keywords/kalman-filter","display_name":"Kalman filter","score":0.15376359224319458},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.11471003293991089},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.0945000946521759}],"concepts":[{"id":"https://openalex.org/C52421305","wikidata":"https://www.wikidata.org/wiki/Q1151499","display_name":"Particle filter","level":3,"score":0.7936058044433594},{"id":"https://openalex.org/C111350023","wikidata":"https://www.wikidata.org/wiki/Q1191869","display_name":"Markov chain Monte Carlo","level":3,"score":0.6904928684234619},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6598051190376282},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.5667502284049988},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5576596856117249},{"id":"https://openalex.org/C159877910","wikidata":"https://www.wikidata.org/wiki/Q2202883","display_name":"Autoregressive model","level":2,"score":0.556186318397522},{"id":"https://openalex.org/C23224414","wikidata":"https://www.wikidata.org/wiki/Q176769","display_name":"Hidden Markov model","level":2,"score":0.5211705565452576},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.4646871089935303},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.4583207666873932},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.45226114988327026},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.44005823135375977},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.35219743847846985},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.33474212884902954},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.2518334686756134},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.22222933173179626},{"id":"https://openalex.org/C157286648","wikidata":"https://www.wikidata.org/wiki/Q846780","display_name":"Kalman filter","level":2,"score":0.15376359224319458},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.11471003293991089},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.0945000946521759}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ssp.2016.7551748","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ssp.2016.7551748","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE Statistical Signal Processing Workshop (SSP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.4099999964237213,"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":22,"referenced_works":["https://openalex.org/W1499550022","https://openalex.org/W1502857830","https://openalex.org/W1886421314","https://openalex.org/W1926440348","https://openalex.org/W1993822631","https://openalex.org/W2025112803","https://openalex.org/W2025262040","https://openalex.org/W2050675057","https://openalex.org/W2069739265","https://openalex.org/W2082542916","https://openalex.org/W2131062138","https://openalex.org/W2131598171","https://openalex.org/W2145125342","https://openalex.org/W2145381462","https://openalex.org/W2158881414","https://openalex.org/W2167783140","https://openalex.org/W2177128624","https://openalex.org/W4244486013","https://openalex.org/W6629976928","https://openalex.org/W6639375475","https://openalex.org/W6640445917","https://openalex.org/W6681608878"],"related_works":["https://openalex.org/W2126907425","https://openalex.org/W2114656557","https://openalex.org/W1499764293","https://openalex.org/W2195963939","https://openalex.org/W2208639223","https://openalex.org/W2514372983","https://openalex.org/W4245379261","https://openalex.org/W2075503097","https://openalex.org/W1497303808","https://openalex.org/W2029412444"],"abstract_inverted_index":{"The":[0,49,81],"Probabilistic":[1],"Acoustic":[2],"Tube":[3],"(PAT)":[4],"model":[5,10,53,57,70,83,88,125],"is":[6,84,126],"a":[7,19,33,85],"probabilistic":[8,34],"generative":[9,16],"of":[11,45,62,73,133],"speech.":[12],"By":[13],"associating":[14],"every":[15,27,39],"parameter":[17],"with":[18,42,140],"probability":[20],"distribution,":[21],"it":[22],"becomes":[23],"possible":[24],"to":[25,128],"convert":[26],"standard":[28],"speech":[29,122],"analysis":[30],"task":[31,41],"into":[32],"inference":[35,94,98],"task,":[36],"thereby":[37],"grounding":[38],"such":[40],"quantifiable":[43],"measures":[44],"bias":[46],"and":[47,59,89,108],"consistency.":[48],"previously":[50],"published":[51],"PAT":[52],"did":[54],"not":[55],"adequately":[56],"AM-FM":[58,72],"therefore":[60],"phase":[61,132],"the":[63,71,74,131,134],"voice":[64,75,135],"source.":[65],"In":[66],"this":[67,124],"paper,":[68],"we":[69],"source":[76],"using":[77,103],"an":[78],"autoregressive":[79],"process.":[80],"resulting":[82],"non-linear":[86],"state-space":[87],"thus":[90],"has":[91],"no":[92],"closed-form":[93],"algorithm,":[95],"but":[96],"effective":[97],"can":[99],"be":[100],"achieved":[101],"by":[102],"Auxiliary":[104],"Particle":[105],"Filtering":[106],"(APF)":[107],"Taylor":[109],"expansion":[110],"assisted":[111],"Markov":[112],"Chain":[113],"Monte":[114],"Carlo":[115],"(MCMC).":[116],"Results":[117],"demonstrate":[118],"that,":[119],"unlike":[120],"previous":[121],"models,":[123],"able":[127],"account":[129],"for":[130],"source,":[136],"achieving":[137],"signal":[138],"reconstruction":[139],"8.79dB":[141],"SNR.":[142]},"counts_by_year":[{"year":2018,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
