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		<title>Machines Learn to Listen: Two Decades of AI-Powered Bioacoustics Reviewed</title>
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		<pubDate>Mon, 05 Oct 2026 05:59:35 +0000</pubDate>
				<category><![CDATA[Technology]]></category>
		<category><![CDATA[bioacoustics]]></category>
		<category><![CDATA[biodiversity monitoring]]></category>
		<category><![CDATA[classification]]></category>
		<category><![CDATA[Conservation]]></category>
		<category><![CDATA[convolutional neural networks]]></category>
		<category><![CDATA[deep learning]]></category>
		<category><![CDATA[Machine Learning]]></category>
		<category><![CDATA[passive acoustic monitoring]]></category>
		<category><![CDATA[preprocessing]]></category>
		<category><![CDATA[Reproducibility]]></category>
		<category><![CDATA[soundscape ecology]]></category>
		<category><![CDATA[systematic review]]></category>
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					<description><![CDATA[A systematic review of 108 studies charts how machine learning transformed bioacoustics over two decades, finding that deep learning now dominates while preprocessing choices and pipeline design, not algorithm families alone, drive classification success.]]></description>
		
		
		
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