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		<title>Human-inspired AI model sharpens sound localization while keeping the network compact</title>
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				<category><![CDATA[Technology]]></category>
		<category><![CDATA[attentional feature fusion]]></category>
		<category><![CDATA[audio sensing]]></category>
		<category><![CDATA[DCASE baseline]]></category>
		<category><![CDATA[direction-of-arrival]]></category>
		<category><![CDATA[distance estimation]]></category>
		<category><![CDATA[human auditory system]]></category>
		<category><![CDATA[Machine Learning]]></category>
		<category><![CDATA[model compactness]]></category>
		<category><![CDATA[neural network]]></category>
		<category><![CDATA[SELDD]]></category>
		<category><![CDATA[sound event localization]]></category>
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					<description><![CDATA[A new human-inspired SELDD neural network cuts angular error from 36.9 to 25.7 degrees and distance error from 0.33 to 0.267 versus the DCASE baseline while staying compact.]]></description>
		
		
		
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