Deployed 2536c93 with MkDocs version: 1.6.1
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<!-- Title -->
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<text x="350" y="22" text-anchor="middle" font-size="14" font-weight="bold" fill="#333">The Cocktail Party Problem</text>
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<text x="55" y="90" text-anchor="middle" font-size="8" fill="#3498db">Speaker 1</text>
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<text x="55" y="155" text-anchor="middle" font-size="8" fill="#e74c3c">Speaker 2</text>
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<text x="185" y="125" text-anchor="middle" font-size="7" fill="#666">Mic</text>
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<!-- Mixed signal -->
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<text x="273" y="92" text-anchor="middle" font-size="8" fill="#333">Mixed Signal</text>
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<text x="406" y="85" text-anchor="middle" font-size="10" font-weight="bold" fill="#333">Separation</text>
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<text x="406" y="100" text-anchor="middle" font-size="10" font-weight="bold" fill="#333">Model</text>
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<text x="406" y="118" text-anchor="middle" font-size="7" fill="#666">(deep network)</text>
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<text x="583" y="90" text-anchor="middle" font-size="8" fill="#3498db">Recovered Source 1</text>
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<text x="583" y="158" text-anchor="middle" font-size="8" fill="#e74c3c">Recovered Source 2</text>
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<!-- Note box -->
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<text x="50" y="205" font-size="10" font-weight="bold" fill="#333">The cocktail party problem:</text>
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<text x="260" y="205" font-size="10" fill="#666">recover individual sources from a mixture.</text>
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<text x="50" y="225" font-size="9" fill="#666">Humans do this effortlessly via auditory attention. Deep learning approaches learn to separate sources</text>
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<text x="50" y="237" font-size="9" fill="#666">from mixed signals, even when trained on synthetic mixtures. Key challenge: the permutation problem.</text>
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