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<!DOCTYPE html> <html> <head><meta http-equiv="Content-Type" content="text/html; charset=utf-8"> <title>Active Noise Cancellation in Microsoft Teams Using AI & NLP Powered Algorithms</title>  <meta charset="UTF-8"> <meta name="viewport" content="width=device-width, initial-scale=1.0"> <meta http-equiv="X-UA-Compatible" content="ie=edge"> <meta name="title" content="Active Noise Cancellation in Microsoft Teams Using AI & NLP Powered Algorithms"> <meta name="description" content="The normal method for analyzing technology is formulating many search queries to extract patent datasets and filter the data physically. The purpose of filtering the collected data is to remove noise to guarantee accurate information analysis. With the advancement in technology and machine learning, the work of physical analysis of the patent can be programmed so the system can remove noise depending on the results based on the previous data. Microsoft team generates a new artificial intelligence model that provides solutions on how individuals respond to speakers. Microsoft team, workplace, Facebook, and Google collected data from many active users hence developing artificial intelligence to minimize distracting background noise, barking and typing during the call" /> <meta name="keywords" content="Artificial intelligence, NLP, Microsoft teams, speech identification, video call, video signal data, machine learning"/>   <meta name="dc.title" content="Active Noise Cancellation in Microsoft Teams Using AI & NLP Powered Algorithms "> <meta name="citation_authors" content="Pawankumar Sharma"> <meta name="citation_authors" content="Bibhu Dash"> <meta name="dc.type" content="Article"> <meta name="dc.source" content="International Journal of Computer Science & Information Technology (IJCSIT) Vol 15, No 1"> <meta name="dc.date" content="2022/02/28"> <meta name="dc.identifier" content="10.5121/ijcsit.2023.15103"> <meta name="dc.publisher" content="AIRCC Publishing Corporation"> <meta name="dc.rights" content="http://creativecommons.org/licenses/by/3.0/"> <meta name="dc.format" content="application/pdf"> <meta name="dc.language" content="en"> <meta name="dc.description" content="The normal method for analyzing technology is formulating many search queries to extract patent datasets and filter the data physically. The purpose of filtering the collected data is to remove noise to guarantee accurate information analysis. With the advancement in technology and machine learning, the work of physical analysis of the patent can be programmed so the system can remove noise depending on the results based on the previous data. Microsoft team generates a new artificial intelligence model that provides solutions on how individuals respond to speakers. Microsoft team, workplace, Facebook, and Google collected data from many active users hence developing artificial intelligence to minimize distracting background noise, barking and typing during the call."/> <meta name="dc.subject" content="Artificial intelligence"> <meta name="dc.subject" content=" NLP"> <meta name="dc.subject" content=" Microsoft teams"> <meta name="dc.subject" content=" speech identification"> <meta name="dc.subject" content=" video call"> <meta name="dc.subject" content=" video signal data"> <meta name="dc.subject" content=" machine learning">   <meta name="prism.publicationName" content="International Journal of Computer Science & Information Technology (IJCSIT) "> <meta name="prism.publicationDate" content="2022/02/28"> <meta name="prism.volume" content="15"> <meta name="prism.number" content="1"> <meta name="prism.section" content="Article"> <meta name="prism.startingPage" content="31">   <meta name="citation_journal_title" content="International Journal of Computer Science & Information Technology (IJCSIT)"> <meta name="citation_publisher" content="AIRCC Publishing Corporation"> <meta name="citation_authors" content="Pawankumar Sharma, Bibhu Dash "> <meta name="citation_title" content="Active Noise Cancellation in Microsoft Teams Using AI & NLP Powered Algorithms"> <meta name="citation_online_date" content="2022/02/28"> <meta name="citation_issue" content="15"> <meta name="citation_firstpage" content="31"> <meta name="citation_authors" content="Pawankumar Sharma"> <meta name="citation_authors" content="Bibhu Dash"> <meta name="citation_doi" content="10.5121/ijcsit.2023.15103"> <meta name="citation_abstract_html_url" content="https://aircconline.com/abstract/ijcsit/v15n1/15123ijcsit03.html"> <meta name="citation_pdf_url" content="https://aircconline.com/ijcsit/V15N1/15123ijcsit03.pdf">   <meta property="og:site_name" content="AIRCC" /> <meta property="og:type" content="article" /> <meta property="og:url" content="https://aircconline.com/abstract/ijcsit/v15n1/15123ijcsit03.html"> <meta property="og:title" content="Active Noise Cancellation in Microsoft Teams Using AI & NLP Powered Algorithms "> <meta property="og:description" content="The normal method for analyzing technology is formulating many search queries to extract patent datasets and filter the data physically. The purpose of filtering the collected data is to remove noise to guarantee accurate information analysis. With the advancement in technology and machine learning, the work of physical analysis of the patent can be programmed so the system can remove noise depending on the results based on the previous data. Microsoft team generates a new artificial intelligence model that provides solutions on how individuals respond to speakers. Microsoft team, workplace, Facebook, and Google collected data from many active users hence developing artificial intelligence to minimize distracting background noise, barking and typing during the call. "/>   <meta name="google-site-verification" content="t8rHIcM8EfjIqfQzQ0IdYIiA9JxDD0uUZAitBCzsOIw" /> <meta name="yandex-verification" content="e3d2d5a32c7241f4" />  <style type="text/css"> a{ color:white; text-decoration:none; } ul li a{ font-weight:bold; color:#000; list-style:none; text-decoration:none; size:10px;} .imagess { height:90px; text-align:left; margin:0px 5px 2px 8px; float:right; border:none; } #left p { font-size:0.90pc; margin-left: 20px; } .right { margin-right: 20px; } #button{ float: left; font-size: 17px; margin-left: 10px; height: 28px; width: 100px; background-color: #1e86c6; } </style> <link rel="icon" type="image/ico" href="/abstract/ico.ico"/> <link rel="stylesheet" type="text/css" href="../main.css" /> </head> <body> <div id="wap"> <div id="page"> <div id="top"> <table width="100%" cellspacing="0" cellpadding="0" > <tr><td colspan="3" valign="top"><img src="../top3.gif" /></td></tr> </table> </div> <div id="menu"> <a href="http://airccse.org/journal/ijcsit.html">Home</a> <a href="http://airccse.org/journal/eboard.html">Editorial</a> <a href="http://airccse.org/journal/papersub.html">Submission</a> <a href="http://airccse.org/journal/jcsit_index.html">Indexing</a> <a href="http://airccse.org/journal/specialissue.html">Special Issue</a> <a href="http://airccse.org/journal/contacts.html">Contacts</a> <a href="http://airccse.org/" target="blank">AIRCC</a></div> <div id="content"> <div id="left"> <h2>Volume 15, Number 1</h2> <h4 style="text-align:center;height:auto"><a>Active Noise Cancellation in Microsoft Teams Using AI & NLP Powered Algorithms</a></h4> <h3>  Authors</h3> <p class="#left">Pawankumar Sharma and Bibhu Dash, University of the Cumberlands, KY USA </p> <h3>  Abstract</h3> <p class="#left right" style="text-align:justify">The normal method for analyzing technology is formulating many search queries to extract patent datasets and filter the data physically. The purpose of filtering the collected data is to remove noise to guarantee accurate information analysis. With the advancement in technology and machine learning, the work of physical analysis of the patent can be programmed so the system can remove noise depending on the results based on the previous data. Microsoft team generates a new artificial intelligence model that provides solutions on how individuals respond to speakers. Microsoft team, workplace, Facebook, and Google collected data from many active users hence developing artificial intelligence to minimize distracting background noise, barking and typing during the call. </p> <h3>  Keywords</h3> <p class="#left right" style="text-align:justify">Artificial intelligence, NLP, Microsoft teams, speech identification, video call, video signal data, machine learning.</p><br> <button type="button" id="button"><a target="blank" href="/ijcsit/V15N1/15123ijcsit03.pdf">Full Text</a></button>   <button type="button" id="button"><a href="http://airccse.org/journal/ijcsit2023_curr.html">Volume 15</a></button> <br><br><br><br><br> </div> </div> <div id="right"> <div class="menu_right"> <ul><li><a href="http://airccse.org/journal/ijcsit.html">Scope & Topics</a></li> <li><a href="http://airccse.org/ethics.html" target="_blank">Ethics</a></li> <li><a href="http://airccse.org/journal/csit_archives.html">Archives</a></li> <li><a href="http://airccse.org/journal/papersub.html">Paper Submission</a></li> <li><a href="http://airccse.org/journal/ijcsit_cited.html">Most Cited Articles</a></li> <li><a href="http://airccse.org/journal/IJCSITleaflet.pdf">Download leaflet</a></li> <li><a href="http://airccse.org/faq.html" target="_blank">FAQ</a></li> </ul> </div><br /> <p align="center"> </p> <p align="center"> </p> </div> <div class="clear"></div> <div id="footer"><table width="100%" ><tr><td height="25" colspan="2"><br /><p align="center">© AIRCC Publishing Corporation</p></td></table> </div> </div> </div> </body> </html>