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              <text>Blind separation of speech from aortic regurgitation signals using Dhoulaths method</text>
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              <text>Blind source component separation; maximum likelihood gradient search; single microphone approach; W-disjoint orthogonality principle</text>
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              <text>Conducting auscultation of traumatically distressed patients has always been demanding for medical professionals. The challenge calls for an innovative solution enabling doctors to conduct precise diagnoses despite other sound interference. This suggested study presents an entirely non-invasive and convenient method designed to aid doctors in routine diagnostic procedures. This study is centred on the segregation of aortic regurgitation heart sounds from speech. The mixture utilised for the study is a combination of speech and aortic regurgitation signals. The method applied for the study is a revised procedure of Blind Source component separation utilising a solo sensor method. With this technique, doctors are not compelled to prevent patients from articulating their pain or discomfort while diagnosing heart sounds. Doctors can offer a consoling word to patients while the auscultation is in progress without worrying about how the speech sounds affect the diagnosis. For babies, timely detection of heart-related issues can be life-saving. With Dhoulaths method, the distressing sounds of a babys cries can be effectively separated, thereby offering doctors clear audio of heartbeats. The study was conducted to ascertain if heartbeats can be segregated from the signals of speech or cries. This segregation procedure has succeeded in arriving at an enhanced level of clarity.  2022 Informa UK Limited, trading as Taylor &amp;amp; Francis Group.</text>
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              <text>Beegum J D.; Chithraprasad D.; Ampadi S.; Poonia R.C.; Sankar M.</text>
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              <text>Journal of Experimental and Theoretical Artificial Intelligence, Vol-36, No. 3, pp. 371-388.</text>
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              <text>Taylor and Francis Ltd.</text>
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              <text>2024-01-01</text>
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              <text>&lt;a href="https://doi.org/10.1080/0952813X.2022.2080868" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1080/0952813X.2022.2080868&lt;/a&gt;
&lt;br /&gt;&lt;br /&gt;&lt;a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85131685111&amp;amp;doi=10.1080%2F0952813X.2022.2080868&amp;amp;partnerID=40&amp;amp;md5=17140803a07459aa94f81ee8953fe72b" target="_blank" rel="noreferrer noopener"&gt;https://www.scopus.com/inward/record.uri?eid=2-s2.0-85131685111&amp;amp;doi=10.1080%2f0952813X.2022.2080868&amp;amp;partnerID=40&amp;amp;md5=17140803a07459aa94f81ee8953fe72b&lt;/a&gt;</text>
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              <text>Restricted Access</text>
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              <text>ISSN: 0952813X</text>
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              <text>Online</text>
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              <text>English</text>
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              <text>Beegum J D., TKM College of Engineering, Kerala, Kollam, India; Chithraprasad D., TKM College of Engineering, Kerala, Kollam, India; Ampadi S., Cardiology, NMCH Brampton, Brampton, Canada; Poonia R.C., Department of Computer Science, CHRIST (Deemed to Be University), Karnataka, Bangalore, India; Sankar M., University of Kerala, Thiruvananthapuram, India</text>
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