ARTIFICIAL INTELLIGENCE DEVICE FOR ROBUST MULTIMODAL ENCODER FOR PERSON REPRESENTATIONS AND CONTROL METHOD THEREOF
A method for controlling an artificial intelligence (AI) device can include obtaining a video sample of a user and an audio sample of the user, generating, via a neural network, a visual embedding based on the video sample and an audio embedding based on the audio sample, the visual embedding and th...
Gespeichert in:
Hauptverfasser: | , |
---|---|
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 | FASHANDI, Homa SELVAKUMARASINGAM, Anith |
description | A method for controlling an artificial intelligence (AI) device can include obtaining a video sample of a user and an audio sample of the user, generating, via a neural network, a visual embedding based on the video sample and an audio embedding based on the audio sample, the visual embedding and the audio embedding being multi-dimensional vectors, generating, via the neural network, an audio-visual embedding based on a combination of the visual and audio embeddings. The method can further include determining a specific pre-enrolled audio-visual embedding from among pre-enrolled audio-visual embeddings corresponding pre-enrolled users based on a distance away from the audio-visual embedding within a joint audio-visual subspace and verifying the user as the specific pre-enrolled user. Also, the neural network can be trained based on a loss function that uses a plurality of audio-visual embeddings, each including an audio component and a visual component. |
format | Patent |
fullrecord | <record><control><sourceid>epo_EVB</sourceid><recordid>TN_cdi_epo_espacenet_US2024347065A1</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>US2024347065A1</sourcerecordid><originalsourceid>FETCH-epo_espacenet_US2024347065A13</originalsourceid><addsrcrecordid>eNqNyrEKwjAQgOEuDqK-w4GzUNuqc0wuNpDm5HJ1LUXiJFqs749FfACnf_i_efZSLM467ZQHFwS9dycMGsHgxU2xxMB0bKNA03pxDZlJToIM8veekSMFYDwzRgyixFGIoIIBTUGYPDQoNRmQGhnJLrPZrb-PafXrIltbFF1v0vDs0jj01_RI766NRV5UZXXI9zu1Lf9TH1qaOa4</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>patent</recordtype></control><display><type>patent</type><title>ARTIFICIAL INTELLIGENCE DEVICE FOR ROBUST MULTIMODAL ENCODER FOR PERSON REPRESENTATIONS AND CONTROL METHOD THEREOF</title><source>esp@cenet</source><creator>FASHANDI, Homa ; SELVAKUMARASINGAM, Anith</creator><creatorcontrib>FASHANDI, Homa ; SELVAKUMARASINGAM, Anith</creatorcontrib><description>A method for controlling an artificial intelligence (AI) device can include obtaining a video sample of a user and an audio sample of the user, generating, via a neural network, a visual embedding based on the video sample and an audio embedding based on the audio sample, the visual embedding and the audio embedding being multi-dimensional vectors, generating, via the neural network, an audio-visual embedding based on a combination of the visual and audio embeddings. The method can further include determining a specific pre-enrolled audio-visual embedding from among pre-enrolled audio-visual embeddings corresponding pre-enrolled users based on a distance away from the audio-visual embedding within a joint audio-visual subspace and verifying the user as the specific pre-enrolled user. Also, the neural network can be trained based on a loss function that uses a plurality of audio-visual embeddings, each including an audio component and a visual component.</description><language>eng</language><subject>ACOUSTICS ; CALCULATING ; COMPUTING ; COUNTING ; MUSICAL INSTRUMENTS ; PHYSICS ; SPEECH ANALYSIS OR SYNTHESIS ; SPEECH OR AUDIO CODING OR DECODING ; SPEECH OR VOICE PROCESSING ; SPEECH RECOGNITION</subject><creationdate>2024</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&date=20241017&DB=EPODOC&CC=US&NR=2024347065A1$$EHTML$$P50$$Gepo$$Hfree_for_read</linktohtml><link.rule.ids>230,308,780,885,25564,76547</link.rule.ids><linktorsrc>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20241017&DB=EPODOC&CC=US&NR=2024347065A1$$EView_record_in_European_Patent_Office$$FView_record_in_$$GEuropean_Patent_Office$$Hfree_for_read</linktorsrc></links><search><creatorcontrib>FASHANDI, Homa</creatorcontrib><creatorcontrib>SELVAKUMARASINGAM, Anith</creatorcontrib><title>ARTIFICIAL INTELLIGENCE DEVICE FOR ROBUST MULTIMODAL ENCODER FOR PERSON REPRESENTATIONS AND CONTROL METHOD THEREOF</title><description>A method for controlling an artificial intelligence (AI) device can include obtaining a video sample of a user and an audio sample of the user, generating, via a neural network, a visual embedding based on the video sample and an audio embedding based on the audio sample, the visual embedding and the audio embedding being multi-dimensional vectors, generating, via the neural network, an audio-visual embedding based on a combination of the visual and audio embeddings. The method can further include determining a specific pre-enrolled audio-visual embedding from among pre-enrolled audio-visual embeddings corresponding pre-enrolled users based on a distance away from the audio-visual embedding within a joint audio-visual subspace and verifying the user as the specific pre-enrolled user. Also, the neural network can be trained based on a loss function that uses a plurality of audio-visual embeddings, each including an audio component and a visual component.</description><subject>ACOUSTICS</subject><subject>CALCULATING</subject><subject>COMPUTING</subject><subject>COUNTING</subject><subject>MUSICAL INSTRUMENTS</subject><subject>PHYSICS</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>2024</creationdate><recordtype>patent</recordtype><sourceid>EVB</sourceid><recordid>eNqNyrEKwjAQgOEuDqK-w4GzUNuqc0wuNpDm5HJ1LUXiJFqs749FfACnf_i_efZSLM467ZQHFwS9dycMGsHgxU2xxMB0bKNA03pxDZlJToIM8veekSMFYDwzRgyixFGIoIIBTUGYPDQoNRmQGhnJLrPZrb-PafXrIltbFF1v0vDs0jj01_RI766NRV5UZXXI9zu1Lf9TH1qaOa4</recordid><startdate>20241017</startdate><enddate>20241017</enddate><creator>FASHANDI, Homa</creator><creator>SELVAKUMARASINGAM, Anith</creator><scope>EVB</scope></search><sort><creationdate>20241017</creationdate><title>ARTIFICIAL INTELLIGENCE DEVICE FOR ROBUST MULTIMODAL ENCODER FOR PERSON REPRESENTATIONS AND CONTROL METHOD THEREOF</title><author>FASHANDI, Homa ; SELVAKUMARASINGAM, Anith</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-epo_espacenet_US2024347065A13</frbrgroupid><rsrctype>patents</rsrctype><prefilter>patents</prefilter><language>eng</language><creationdate>2024</creationdate><topic>ACOUSTICS</topic><topic>CALCULATING</topic><topic>COMPUTING</topic><topic>COUNTING</topic><topic>MUSICAL INSTRUMENTS</topic><topic>PHYSICS</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>FASHANDI, Homa</creatorcontrib><creatorcontrib>SELVAKUMARASINGAM, Anith</creatorcontrib><collection>esp@cenet</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>FASHANDI, Homa</au><au>SELVAKUMARASINGAM, Anith</au><format>patent</format><genre>patent</genre><ristype>GEN</ristype><title>ARTIFICIAL INTELLIGENCE DEVICE FOR ROBUST MULTIMODAL ENCODER FOR PERSON REPRESENTATIONS AND CONTROL METHOD THEREOF</title><date>2024-10-17</date><risdate>2024</risdate><abstract>A method for controlling an artificial intelligence (AI) device can include obtaining a video sample of a user and an audio sample of the user, generating, via a neural network, a visual embedding based on the video sample and an audio embedding based on the audio sample, the visual embedding and the audio embedding being multi-dimensional vectors, generating, via the neural network, an audio-visual embedding based on a combination of the visual and audio embeddings. The method can further include determining a specific pre-enrolled audio-visual embedding from among pre-enrolled audio-visual embeddings corresponding pre-enrolled users based on a distance away from the audio-visual embedding within a joint audio-visual subspace and verifying the user as the specific pre-enrolled user. Also, the neural network can be trained based on a loss function that uses a plurality of audio-visual embeddings, each including an audio component and a visual component.</abstract><oa>free_for_read</oa></addata></record> |
fulltext | fulltext_linktorsrc |
identifier | |
ispartof | |
issn | |
language | eng |
recordid | cdi_epo_espacenet_US2024347065A1 |
source | esp@cenet |
subjects | ACOUSTICS CALCULATING COMPUTING COUNTING MUSICAL INSTRUMENTS PHYSICS SPEECH ANALYSIS OR SYNTHESIS SPEECH OR AUDIO CODING OR DECODING SPEECH OR VOICE PROCESSING SPEECH RECOGNITION |
title | ARTIFICIAL INTELLIGENCE DEVICE FOR ROBUST MULTIMODAL ENCODER FOR PERSON REPRESENTATIONS AND CONTROL METHOD THEREOF |
url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-05T16%3A30%3A37IST&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=FASHANDI,%20Homa&rft.date=2024-10-17&rft_id=info:doi/&rft_dat=%3Cepo_EVB%3EUS2024347065A1%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 |