flykhan
2536c937e3
翻译自英文原版 maths-cs-ai-compendium，共 20 章全部完成。 第01章 向量 | 第02章 矩阵 | 第03章 微积分 第04章 统计学 | 第05章 概率论 | 第06章 机器学习 第07章 计算语言学 | 第08章 计算机视觉 | 第09章 音频与语音 第10章 多模态学习 | 第11章 自主系统 | 第12章 图神经网络 第13章 计算与操作系统 | 第14章 数据结构与算法 第15章 生产级软件工程 | 第16章 SIMD与GPU编程 第17章 AI推理 | 第18章 ML系统设计 第19章 应用人工智能 | 第20章 前沿人工智能 翻译说明： - 所有数学公式 $...$ / $$...$$、代码块、图片引用完整保留 - mkdocs.yml 配置中文导航 + language: zh - README.md 已翻译为中文（兼 docs/index.md） - docs/ 目录包含指向各章文件的 symlink - 约 29,000 行中文内容，排除 .cache/ 构建缓存
109 lines
7.0 KiB
XML
109 lines
7.0 KiB
XML
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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">Conv-TasNet Architecture</text>
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<text x="85" y="42" text-anchor="middle" font-size="9" font-weight="bold" fill="#3498db">Encoder</text>
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<text x="330" y="42" text-anchor="middle" font-size="9" font-weight="bold" fill="#9b59b6">Separator (TCN)</text>
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<text x="590" y="42" text-anchor="middle" font-size="9" font-weight="bold" fill="#27ae60">Decoder</text>
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<text x="60" y="105" text-anchor="middle" font-size="8" fill="#333">Mixture x(t)</text>
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<!-- Encoder -->
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<text x="158" y="70" text-anchor="middle" font-size="9" fill="#333">Encoder</text>
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<text x="158" y="83" text-anchor="middle" font-size="7" fill="#666">1D Conv</text>
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<text x="158" y="93" text-anchor="middle" font-size="7" fill="#666">+ ReLU</text>
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<!-- Encoded mixture representation -->
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<text x="248" y="72" text-anchor="middle" font-size="7" fill="#333">Encoded</text>
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<text x="248" y="82" text-anchor="middle" font-size="7" fill="#333">Mixture W</text>
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<!-- TCN Separator -->
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<!-- TCN blocks with skip connections -->
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<text x="363" y="75" text-anchor="middle" font-size="7" fill="#333">1x1 Conv + PReLU + Norm</text>
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<text x="363" y="103" text-anchor="middle" font-size="7" fill="#333">D-Conv (dilation 2^k)</text>
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<text x="363" y="131" text-anchor="middle" font-size="7" fill="#333">1x1 Conv (skip + res)</text>
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<!-- Skip connection line -->
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<text x="440" y="95" font-size="6" fill="#f39c12">skip</text>
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<!-- Residual path -->
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<text x="300" y="100" font-size="6" fill="#27ae60" text-anchor="end">res</text>
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<text x="363" y="150" text-anchor="middle" font-size="7" fill="#9b59b6">x R repeats</text>
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<!-- Mask outputs -->
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<text x="363" y="168" text-anchor="middle" font-size="7" fill="#333">Sigmoid masks (M1, M2)</text>
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<!-- Apply masks -->
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<!-- Mask application -->
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<text x="481" y="72" text-anchor="middle" font-size="8" fill="#333">Apply</text>
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<text x="481" y="83" text-anchor="middle" font-size="8" fill="#333">Masks</text>
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<text x="481" y="105" text-anchor="middle" font-size="7" fill="#666">W * M_i</text>
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<!-- Mask connection from TCN bottom to Apply -->
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<!-- Decoder -->
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<text x="570" y="70" text-anchor="middle" font-size="9" fill="#333">Decoder</text>
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<text x="570" y="83" text-anchor="middle" font-size="7" fill="#666">Transposed</text>
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<text x="570" y="93" text-anchor="middle" font-size="7" fill="#666">1D Conv</text>
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<!-- Output separated waveforms -->
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<text x="663" y="75" text-anchor="middle" font-size="7" fill="#3498db">s1(t)</text>
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<text x="663" y="122" text-anchor="middle" font-size="7" fill="#e74c3c">s2(t)</text>
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<!-- Encoder-to-Mask connection (skip from encoded to mask apply) -->
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<text x="360" y="200" text-anchor="middle" font-size="7" fill="#3498db">encoded mixture W passed to mask application</text>
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<!-- Note box -->
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<text x="50" y="235" font-size="10" font-weight="bold" fill="#333">Time-domain approach:</text>
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<text x="197" y="235" font-size="10" fill="#666">works directly on waveforms, no STFT needed.</text>
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<text x="50" y="252" font-size="9" fill="#666">The encoder learns a task-optimal representation. The TCN separator produces masks in the learned basis,</text>
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<text x="50" y="264" font-size="9" fill="#666">and the decoder reconstructs the separated waveforms. Achieves strong SI-SDR on speech separation benchmarks.</text>
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