Sim-to-real learning of 2D multiple sound source localization

A computer-implemented method is provided for training a multi-source sound localization model using labeled simulation data and unlabeled real data. The method includes inputting the labeled simulation data and the unlabeled real data respectively into a multi-source sound localization model of a n...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Hauptverfasser: Le Moing, Guillaume Jean Victor Marie, Agravante, Don Joven Ravoy, Vinayavekhin, Phongtharin, Inoue, Tadanobu, Vongkulbhisal, Jayakorn, Munawar, Asim
Format: Patent
Sprache:eng
Schlagworte:
Online-Zugang:Volltext bestellen
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page
container_issue
container_start_page
container_title
container_volume
creator Le Moing, Guillaume Jean Victor Marie
Agravante, Don Joven Ravoy
Vinayavekhin, Phongtharin
Inoue, Tadanobu
Vongkulbhisal, Jayakorn
Munawar, Asim
description A computer-implemented method is provided for training a multi-source sound localization model using labeled simulation data and unlabeled real data. The method includes inputting the labeled simulation data and the unlabeled real data respectively into a multi-source sound localization model of a neural network to obtain a localization heatmap from an output layer of the multi-source sound localization model for each of the labeled simulation data and the unlabeled real data. The method further includes inputting the localization heatmap for each of the labeled simulation data and the unlabeled real data into an output discriminator. The method also includes training the output discriminator so that the output discriminator assigns a domain class label to distinguish simulation data from real data. The method additionally includes training, by a hardware process, the multi-source sound localization model by a first adversarial loss for the output discriminator with an original localization model loss.
format Patent
fullrecord <record><control><sourceid>epo_EVB</sourceid><recordid>TN_cdi_epo_espacenet_US11676032B2</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>US11676032B2</sourcerecordid><originalsourceid>FETCH-epo_espacenet_US11676032B23</originalsourceid><addsrcrecordid>eNrjZLANzszVLcnXLUpNzFHISU0sysvMS1fIT1MwclHILc0pySzISVUozi_NSwGRRcmpCjn5yYk5mVWJJZn5eTwMrGmJOcWpvFCam0HRzTXE2UM3tSA_PrW4IDE5NS-1JD402NDQzNzMwNjIyciYGDUA66cwAA</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>patent</recordtype></control><display><type>patent</type><title>Sim-to-real learning of 2D multiple sound source localization</title><source>esp@cenet</source><creator>Le Moing, Guillaume Jean Victor Marie ; Agravante, Don Joven Ravoy ; Vinayavekhin, Phongtharin ; Inoue, Tadanobu ; Vongkulbhisal, Jayakorn ; Munawar, Asim</creator><creatorcontrib>Le Moing, Guillaume Jean Victor Marie ; Agravante, Don Joven Ravoy ; Vinayavekhin, Phongtharin ; Inoue, Tadanobu ; Vongkulbhisal, Jayakorn ; Munawar, Asim</creatorcontrib><description>A computer-implemented method is provided for training a multi-source sound localization model using labeled simulation data and unlabeled real data. The method includes inputting the labeled simulation data and the unlabeled real data respectively into a multi-source sound localization model of a neural network to obtain a localization heatmap from an output layer of the multi-source sound localization model for each of the labeled simulation data and the unlabeled real data. The method further includes inputting the localization heatmap for each of the labeled simulation data and the unlabeled real data into an output discriminator. The method also includes training the output discriminator so that the output discriminator assigns a domain class label to distinguish simulation data from real data. The method additionally includes training, by a hardware process, the multi-source sound localization model by a first adversarial loss for the output discriminator with an original localization model loss.</description><language>eng</language><subject>ACOUSTICS ; CALCULATING ; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS ; COMPUTING ; COUNTING ; DEAF-AID SETS ; ELECTRIC COMMUNICATION TECHNIQUE ; ELECTRICITY ; LOUDSPEAKERS, MICROPHONES, GRAMOPHONE PICK-UPS OR LIKEACOUSTIC ELECTROMECHANICAL TRANSDUCERS ; MUSICAL INSTRUMENTS ; PHYSICS ; PUBLIC ADDRESS SYSTEMS ; SPEECH ANALYSIS OR SYNTHESIS ; SPEECH OR AUDIO CODING OR DECODING ; SPEECH OR VOICE PROCESSING ; SPEECH RECOGNITION</subject><creationdate>2023</creationdate><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&amp;date=20230613&amp;DB=EPODOC&amp;CC=US&amp;NR=11676032B2$$EHTML$$P50$$Gepo$$Hfree_for_read</linktohtml><link.rule.ids>230,308,780,885,25563,76318</link.rule.ids><linktorsrc>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&amp;date=20230613&amp;DB=EPODOC&amp;CC=US&amp;NR=11676032B2$$EView_record_in_European_Patent_Office$$FView_record_in_$$GEuropean_Patent_Office$$Hfree_for_read</linktorsrc></links><search><creatorcontrib>Le Moing, Guillaume Jean Victor Marie</creatorcontrib><creatorcontrib>Agravante, Don Joven Ravoy</creatorcontrib><creatorcontrib>Vinayavekhin, Phongtharin</creatorcontrib><creatorcontrib>Inoue, Tadanobu</creatorcontrib><creatorcontrib>Vongkulbhisal, Jayakorn</creatorcontrib><creatorcontrib>Munawar, Asim</creatorcontrib><title>Sim-to-real learning of 2D multiple sound source localization</title><description>A computer-implemented method is provided for training a multi-source sound localization model using labeled simulation data and unlabeled real data. The method includes inputting the labeled simulation data and the unlabeled real data respectively into a multi-source sound localization model of a neural network to obtain a localization heatmap from an output layer of the multi-source sound localization model for each of the labeled simulation data and the unlabeled real data. The method further includes inputting the localization heatmap for each of the labeled simulation data and the unlabeled real data into an output discriminator. The method also includes training the output discriminator so that the output discriminator assigns a domain class label to distinguish simulation data from real data. The method additionally includes training, by a hardware process, the multi-source sound localization model by a first adversarial loss for the output discriminator with an original localization model loss.</description><subject>ACOUSTICS</subject><subject>CALCULATING</subject><subject>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</subject><subject>COMPUTING</subject><subject>COUNTING</subject><subject>DEAF-AID SETS</subject><subject>ELECTRIC COMMUNICATION TECHNIQUE</subject><subject>ELECTRICITY</subject><subject>LOUDSPEAKERS, MICROPHONES, GRAMOPHONE PICK-UPS OR LIKEACOUSTIC ELECTROMECHANICAL TRANSDUCERS</subject><subject>MUSICAL INSTRUMENTS</subject><subject>PHYSICS</subject><subject>PUBLIC ADDRESS SYSTEMS</subject><subject>SPEECH ANALYSIS OR SYNTHESIS</subject><subject>SPEECH OR AUDIO CODING OR DECODING</subject><subject>SPEECH OR VOICE PROCESSING</subject><subject>SPEECH RECOGNITION</subject><fulltext>true</fulltext><rsrctype>patent</rsrctype><creationdate>2023</creationdate><recordtype>patent</recordtype><sourceid>EVB</sourceid><recordid>eNrjZLANzszVLcnXLUpNzFHISU0sysvMS1fIT1MwclHILc0pySzISVUozi_NSwGRRcmpCjn5yYk5mVWJJZn5eTwMrGmJOcWpvFCam0HRzTXE2UM3tSA_PrW4IDE5NS-1JD402NDQzNzMwNjIyciYGDUA66cwAA</recordid><startdate>20230613</startdate><enddate>20230613</enddate><creator>Le Moing, Guillaume Jean Victor Marie</creator><creator>Agravante, Don Joven Ravoy</creator><creator>Vinayavekhin, Phongtharin</creator><creator>Inoue, Tadanobu</creator><creator>Vongkulbhisal, Jayakorn</creator><creator>Munawar, Asim</creator><scope>EVB</scope></search><sort><creationdate>20230613</creationdate><title>Sim-to-real learning of 2D multiple sound source localization</title><author>Le Moing, Guillaume Jean Victor Marie ; Agravante, Don Joven Ravoy ; Vinayavekhin, Phongtharin ; Inoue, Tadanobu ; Vongkulbhisal, Jayakorn ; Munawar, Asim</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-epo_espacenet_US11676032B23</frbrgroupid><rsrctype>patents</rsrctype><prefilter>patents</prefilter><language>eng</language><creationdate>2023</creationdate><topic>ACOUSTICS</topic><topic>CALCULATING</topic><topic>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</topic><topic>COMPUTING</topic><topic>COUNTING</topic><topic>DEAF-AID SETS</topic><topic>ELECTRIC COMMUNICATION TECHNIQUE</topic><topic>ELECTRICITY</topic><topic>LOUDSPEAKERS, MICROPHONES, GRAMOPHONE PICK-UPS OR LIKEACOUSTIC ELECTROMECHANICAL TRANSDUCERS</topic><topic>MUSICAL INSTRUMENTS</topic><topic>PHYSICS</topic><topic>PUBLIC ADDRESS SYSTEMS</topic><topic>SPEECH ANALYSIS OR SYNTHESIS</topic><topic>SPEECH OR AUDIO CODING OR DECODING</topic><topic>SPEECH OR VOICE PROCESSING</topic><topic>SPEECH RECOGNITION</topic><toplevel>online_resources</toplevel><creatorcontrib>Le Moing, Guillaume Jean Victor Marie</creatorcontrib><creatorcontrib>Agravante, Don Joven Ravoy</creatorcontrib><creatorcontrib>Vinayavekhin, Phongtharin</creatorcontrib><creatorcontrib>Inoue, Tadanobu</creatorcontrib><creatorcontrib>Vongkulbhisal, Jayakorn</creatorcontrib><creatorcontrib>Munawar, Asim</creatorcontrib><collection>esp@cenet</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Le Moing, Guillaume Jean Victor Marie</au><au>Agravante, Don Joven Ravoy</au><au>Vinayavekhin, Phongtharin</au><au>Inoue, Tadanobu</au><au>Vongkulbhisal, Jayakorn</au><au>Munawar, Asim</au><format>patent</format><genre>patent</genre><ristype>GEN</ristype><title>Sim-to-real learning of 2D multiple sound source localization</title><date>2023-06-13</date><risdate>2023</risdate><abstract>A computer-implemented method is provided for training a multi-source sound localization model using labeled simulation data and unlabeled real data. The method includes inputting the labeled simulation data and the unlabeled real data respectively into a multi-source sound localization model of a neural network to obtain a localization heatmap from an output layer of the multi-source sound localization model for each of the labeled simulation data and the unlabeled real data. The method further includes inputting the localization heatmap for each of the labeled simulation data and the unlabeled real data into an output discriminator. The method also includes training the output discriminator so that the output discriminator assigns a domain class label to distinguish simulation data from real data. The method additionally includes training, by a hardware process, the multi-source sound localization model by a first adversarial loss for the output discriminator with an original localization model loss.</abstract><oa>free_for_read</oa></addata></record>
fulltext fulltext_linktorsrc
identifier
ispartof
issn
language eng
recordid cdi_epo_espacenet_US11676032B2
source esp@cenet
subjects ACOUSTICS
CALCULATING
COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
COMPUTING
COUNTING
DEAF-AID SETS
ELECTRIC COMMUNICATION TECHNIQUE
ELECTRICITY
LOUDSPEAKERS, MICROPHONES, GRAMOPHONE PICK-UPS OR LIKEACOUSTIC ELECTROMECHANICAL TRANSDUCERS
MUSICAL INSTRUMENTS
PHYSICS
PUBLIC ADDRESS SYSTEMS
SPEECH ANALYSIS OR SYNTHESIS
SPEECH OR AUDIO CODING OR DECODING
SPEECH OR VOICE PROCESSING
SPEECH RECOGNITION
title Sim-to-real learning of 2D multiple sound source localization
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-11T23%3A19%3A25IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-epo_EVB&rft_val_fmt=info:ofi/fmt:kev:mtx:patent&rft.genre=patent&rft.au=Le%20Moing,%20Guillaume%20Jean%20Victor%20Marie&rft.date=2023-06-13&rft_id=info:doi/&rft_dat=%3Cepo_EVB%3EUS11676032B2%3C/epo_EVB%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rfr_iscdi=true