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	<title>MC-RemixIT &#8211; BIOENGINEER.ORG</title>
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		<title>Self-Teaching AI Untangles Critically Endangered Gibbon Calls From Rainforest Noise</title>
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		<pubDate>Sun, 25 Oct 2026 06:08:55 +0000</pubDate>
				<category><![CDATA[Biology]]></category>
		<category><![CDATA[bioacoustics]]></category>
		<category><![CDATA[call detection]]></category>
		<category><![CDATA[conservation technology]]></category>
		<category><![CDATA[deep learning]]></category>
		<category><![CDATA[DNSMOS]]></category>
		<category><![CDATA[Hainan gibbon]]></category>
		<category><![CDATA[MC-RemixIT]]></category>
		<category><![CDATA[passive acoustic monitoring]]></category>
		<category><![CDATA[Self-Supervised Learning]]></category>
		<category><![CDATA[source separation]]></category>
		<category><![CDATA[teacher-student framework]]></category>
		<category><![CDATA[tropical rainforest soundscapes]]></category>
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					<description><![CDATA[Researchers developed MC-RemixIT, a self-supervised AI framework that separates Hainan gibbon calls from complex rainforest noise without clean reference recordings and improved downstream call detection accuracy by up to roughly ten percentage points.]]></description>
		
		
		
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