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- 5.1.4 分析环境准备
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@@ -2249,7 +2236,7 @@ 数ֵ
在 CD 时代伊始，音频的格式就从传统的纯物理记录方式，演变成了调制解调（PCM）配合格式压缩存储的处理过程。这正是数字时代和以往传统时代相比，最为显著的特征。
因此，想要理解并处理音频，首先需要从如何衡量 声音（Sounds） 开始。
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@@ -2312,7 +2299,7 @@ 频率ÿ
观察例举的统计结果，会发现直觉上非常吵闹的声音，如飞机发动机的声音，其频率并不一定高。而一些我们生活中感觉难以察觉的声音，如蚊子飞行声，却不一定低频。
显然，频率并不能代表声音的高低 。我们还需要其它参数表示，那就是 响度（Loudness） 。
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@@ -2292,7 +2279,7 @@ N ∑ = 1 0 ⋅ log 1 0 ( ∑ n ( p r e f p i ) 2 ) = 1 0 ⋅ log 1 0 ( ∑ n ( I r e f I i ) ) = 1 0 ⋅ log 1 0 ( 1 0 1 0 d B L 0 + 1 0 1 0 d B L 1 + ⋯ + 1 0 1 0 d B L n )
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@@ -2074,25 +2074,12 @@
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-
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@@ -2239,7 +2226,7 @@ 谐波
可见，决定整个谐波链的关键，就在于第一谐波，也就是基波上。而在基波响度相同的情况下，产生的第二、第三、... 、第 i 谐波，其 数目 和 各自的响度 ，才确定了声源特色。
至此，声音三要素与工程量映射，就解释清楚了。
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@@ -2173,7 +2160,7 @@ 1.4 声音的
不同角度观察到的，可以认为是同一声音在各自领域平面的投影。而我们通过这种方式，从不同的视角，拼接出了声音本身。所以，声音也可以被称为是某种程度上的高维信息。 并非 在不考虑传播时，直觉上的仅有时频那么简单。
接下来，我们便分别从这三个不同的视角，去看如何处理。
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那么，其具体是怎样的测量过程，而结果又是怎样体现的呢？
这就需要提到 等响曲线（Equal Loudness Level Contour） 了。
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而将声音从物理波转为数字保存，并在需要时提供还原能力的技术，就是 调制解调（Modulation & Demodulation） 技术。
这既是本节讨论的内容。
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由于参考电压一般要求稳定，所以至少需要以内部元件提供稳定三相电来作为基准。不过，对于精度要求极低的设备，为了电子组件复用和电路板的简化，会采用把采样时钟信号的电压作为参考输入的非常做法。但对于高精度设备（包括麦克风等），时钟信号为高频信号，是严格不能作为参考输入的。
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@@ -2221,7 +2208,7 @@ PCM & PDM 异
音视频工程场景中，我们常处理的音频信号，基本为 PCM 方式获取的数字信号。 对于想要进行调整的 PDM 数字信号，通常需要转换为 PCM 数字信号后，再行以 PCM 更具优势的直接编辑方式，进行相关操作。而位于计算机体系内用来实现音频存储的数字信号基础类型，亦为 PCM 类型的数字信号。
由此可见 PCM 数字信号的重要性。
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@@ -2173,7 +2160,7 @@ 1.6 音频的
这就是音频存储的基础格式，PCM 音频格式 。
什么是音频格式？
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然而，尽管无损压缩如 FLAC 提供了最高的音质保真度，但其文件大小仍然相对较大 。在许多应用场景中，如 流媒体 和 便携设备存储（尤其是在随身听时代，早期有限的存储空间情况） ，依然 不够便利 。因此，具有更大压缩比的有损压缩编码音频格式 ，如 MP3 和 AAC 便成为了一种 可以接受的替代方案 。这些格式 通过舍弃人耳不易察觉的音频信息，进一步减小文件大小，同时在音质和压缩率之间取得平衡 。
虽然为人所带来的听觉感受，介于此，会相对有所衰减。
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[26] Roberts Family. FLAC Metadata Structure [EB/OL]. [2023-10-23]. https://www.the-roberts-family.net/metadata/flac.html .
[27] Theile, Günther; Stolle, Gerhard; 1992; MUSICAM-Surround: A Universal Multichannel Coding System Compatible with ISO 11172-3 PDF ; Institut fur Rundfunktechnik, Munich, Germany; Paper 3403; Available from: https://aes2.org/publications/elibrary-page/?id=6731
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混合律是对加法混合论的一次成功拓展，此时已经隐约可以看到最初色度图的理论雏形了。不过这时对颜色的索引还停留在比较初级的阶段。现代学界和工业界已普遍采用 色度（Chromaticity） ，配合 颜色空间（Color Space） ，来代替描述颜色种类。色调更多的被用于艺术和设计领域。
另一方面，随着 现代色彩体系（Modern Color System） 的在细分领域的逐步分化，部分颜色空间的规格出发点，也对色调（Hue）和饱和度（Saturation）代表的概念本身进行了充分的抽象，形成了诸如 LAB、LUV 和 颜色三要素（HSL）等经典的色彩空间方案 。为当代计算机工业体系中，艺术设计、数据传输和工程计算方面的贯通，提供了较大的帮助（可参见后文 2.5 经典色彩空间 ）。
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在已知白点（White Point）和选定色的情况下。依据格拉斯曼饱和度取值，人们可以计算得期望的渐变色泽，从而快速调色。
同 色调（Hue） 一样， 饱和度（Saturation） 也处于简单系统中，不方便体系下的量化。因此，饱和度的概念在现代学界和工业界中，同样也普遍被色度（Chromaticity）配合颜色空间（Color Space）代替表示 ，以便于工程量化计算。 现代色彩体系（Modern Color System） 中的部分方案，对饱和度概念进行了有效的利用转换（可参见后文 2.5.7 颜色三要素色彩空间 ）。
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@@ -2292,7 +2279,7 @@ F ( C ) F(C) F ( C ) ，我们就可以将色温为 T 0 T_{0} T 0 F ( C 0 , L 0 ) F(C_0,\ L_0) F ( C 0 , L 0 ) F ( C ) F(C) F ( C ) 配色函数（Color-Matching Functions） 。
只要能找到适合的 F ( C ) F(C) F ( C )
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@@ -2172,7 +2159,7 @@ 2.4 色彩的
自 1931 年 CIE RGB & CIE XYZ 色彩空间 [12] 被提出后，色彩在工程中的对比标准就被统一在了 CIE 逐步采纳、整理和定义的 一系列规格之下 。而 CIE XYZ 色彩空间具有直观客观和正向全可见光色域的特点，使得它更适合被用来作为工业应用的基准体系。所以，我们往往都会将需要处理的颜色数据， 转换到 CIE XYZ 之下进行权衡 。
当然，整个 CIE 色彩空间体系，其提出迭代的过程和当下的统治地位也并不是一蹴而就。这里先对工程上由 CIE 规范的关键概念进行介绍。以便于为我们更好的理解后续章节中，不同色彩空间的提出背景和针对性解决的问题困难，提供帮助。
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@@ -2362,7 +2349,7 @@ x y = ⎩ ⎪ ⎪ ⎨ ⎪ ⎪ ⎧ − 0 . 2 6 6 1 2 3 9 ⋅ T c 3 1 0 9 − 0 . 2 3 4 3 5 8 9 ⋅ T c 2 1 0 6 + 0 . 8 7 7 6 9 5 6 ⋅ T 1 0 3 c + 0 . 1 7 9 9 1 0 − 3 . 0 2 5 8 4 6 9 ⋅ T c 3 1 0 9 + 2 . 1 0 7 0 3 7 9 ⋅ T c 2 1 0 6 + 0 . 2 2 2 6 3 4 7 ⋅ T 1 0 3 c + 0 . 2 4 0 3 9 0 1 6 6 7 K ≤ T c ≤ 4 0 0 0 K 4 0 0 0 K ≤ T c ≤ 2 5 0 0 0 K = ⎩ ⎪ ⎨ ⎪ ⎧ − 1 . 1 0 6 3 8 1 4 ⋅ x 3 − 1 . 3 4 8 1 1 0 2 0 ⋅ x 2 + 2 . 1 8 5 5 5 8 3 2 ⋅ x − 0 . 2 0 2 1 9 6 8 3 − 0 . 9 5 4 9 4 7 6 ⋅ x 3 − 1 . 3 7 4 1 8 5 9 3 ⋅ x 2 + 2 . 0 9 1 3 7 0 1 5 ⋅ x − 0 . 1 6 7 4 8 8 6 7 + 3 . 0 8 1 7 5 8 0 ⋅ x 3 − 5 . 8 7 3 3 8 6 7 0 ⋅ x 2 + 3 . 7 5 1 1 2 9 9 7 ⋅ x − 0 . 3 7 0 0 1 4 8 3 1 6 6 7 K ≤ T c ≤ 2 2 2 2 K 2 2 2 2 K ≤ T c ≤ 4 0 0 0 K 4 0 0 0 K ≤ T c ≤ 2 5 0 0 0 K
但是这一套算法，仍然无法代替非精确场景下，直接通过对应物理色温计算普朗克轨迹上色度的方法实用。因此，CIE 也和麦卡米指数逼近的情况一样，仅是将其列入了相关色温在需求精确值情况下的补充。这里有所了解即可。
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@@ -2204,7 +2191,7 @@ ⎣ ⎡ C ′ M ′ Y ′ ⎦ ⎤ = ⎣ ⎡ ( 1 − K ) ⋅ C + K ( 1 − K ) ⋅ M + K ( 1 − K ) ⋅ Y + K ⎦ ⎤
而对于 CYMK 色彩空间和 RGB 色彩空间互转，就有需要以 CMY 色彩空间作为桥梁。先根据转换方向，通过 CMY 色彩空间进行 C R G B → C C M Y C_{RGB} \rightarrow C_{CMY} C R G B → C C M Y C C M Y K → C C M Y C_{CMYK} \rightarrow C_{CMY} C C M Y K → C C M Y
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因此，CIE RGB 也不可避免的继承了光学三原色色彩空间的 负色匹配 问题。
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随着 19世纪 80年代个人电脑的快速发展。灰度图格式也从 单字节码（1-bit） ，经过 IBM 单色显示屏适配器（MDA [Monochrome Display Adapter]） 2-bit 格式 ，Commodore 128 所搭载 8563 显示控制器（VDC [Video Display Controller]） 提供的 4-bit 格式 ，演变到了Apple II 与 IBM 5150 的 8-bit 单色格式 。
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此特点决定了，当市面上绝大多数显卡的存储及处理能力没有发展的情况下，更细腻的 RGB 色彩格式也不太可能得到推广。同理，广泛应用于图像传输的 YUV 色彩格式则是规格驱动，其更多依赖于传输协议的演变和数据带宽的更新迭代。
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至此，有关音视频工程中的图片色彩处理部分，基本讲解完毕。下一章我们将利用目前已掌握的音视频知识，来做针对一段音频和一张图片基本分析的工程实践。
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- 5.1.4 分析环境准备
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@@ -2219,7 +2206,7 @@ Ө
[48] ITU-R, Rec. ITU-R BT.2020-2, "BT.2020 : Parameter values for ultra-high definition television systems for production and international programme exchange", Article Number E 70000, archived from the original on 2015-10-14
[49] 雷霄骅, "Color format conversion: The simplest example of libswscale based on FFmpeg (YUV to RGB)", archived (Web: https://blog.csdn.net/leixiaohua1020/article/details/42134965 ) from the original on 2014-12-28
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这种多梯度的方式，会增强算法对图像边缘的处理能力，保存边缘的同时增强细节。因此也被称为 边缘锐化（Edge Sharpening） 。
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@@ -2891,7 +2878,7 @@ HO
HOG 数据帧（HOG Frame）更多被作为经过特征提取后的预处理输入数据，传入目标物体检测等人工智能计算机视觉方向的算法模型。 通过模型获取的物体识别结果后，再利用训练好的目标跟踪模型，或传统目标跟踪算法（诸如：核卷积滤波（KCF [Kernelized Correlation Filter]）[18] 、MOSSE 算法等）等，来获取视频流中运动物体在时序上的关联性。
那么，用于判断目标检测结果是否准确的方法，也就是目标检测模型的 损失函数（Loss Function） 是什么呢？
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代入图像大小获得配置，来快速获取包含完整被检测物体的闭包，方便模型处理得到目标实际区域，并工程缩减模型的输入。
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[34] Salehifar M, Koo M, Lim J, et al. CE 6.2. 6: Reduced Secondary Transform (RST)[J]. Joint Video Experts Team (JVET) of ITU-T SG, 2018, 16: 10-18.
[35] Koo M, Salehifar M, Lim J, et al. CE6: reduced secondary transform (RST)(CE6-3.1)[J]. Joint Video Experts Team (JVET) of ITU-T SG, 2019, 16: 19-27.
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皆为训练过程中的 样本量级参数 。
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即，组成嵌入集的特征向量形式，并没有特殊的要求。但往往需要根据采用的损失函数来决定最终的格式。这一点在实践中非常重要。由于评估数据常用于线性回归，区别起见被称为 预测集（Predictions） 。
现在，我们基本掌握了深度学习的入门概念。让我们分步来看，一个神经网络的具体细节。
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当然，连接函数并不只有列出的这三种类型。每年都有大量有关此方面的研究，给出新的样式。但从上我们也能够发现，若非足够泛化，连接函数鲜有脱离模型而存在的独立类型 。这在上文中列出的 Maxout 与 Dropout、Softmax 的对比中有明显体现。因此，需要在训练中注意这一点。
目前，我们已经掌握了基本的样本提炼手段， 接下来就需要考虑权重迭代了。
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正则项（Regularities） ，用于衡量模型 复杂度（complexity） 的损失函数组成部分。衡量模型复杂度的方法有很多。大部分是从权重对整个模型影响的层面来判断的，即从权重的大小，来衡量某个参数对整体模型的影响。
接下来，我们就分别从 回归项（Regression）、分类项（Classification）、正则项（Regularities）三种类型，来了解损失函数的使用。
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对组排异损失从样本宏观角度，统一了正负样本概念。指明了，非当前指向类的负样本，可以被认为是指向负样本类型情况的正样本。因此，对于 N 分类处理过程，整个运算损失计算时间复杂度被化简为仅有 2N。相当的高效。
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L-1 惩罚项（L 1 L_1 L 1 由于其特性，常被用于裁剪参数数量，缩减模型宽度。从另一种角度来理解，可以认为 L-1 的思想其实和 Maxout 激活函数的思想有些类似。都是通过线性关系，来整合实际特征曲线。只不过 L-1 是从模型复杂度的角度，Maxout 是从非线性特征的角度。
L-1 惩罚项被证明，对于稀疏性模型优化非常有效。
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至此，损失函数的三类组成部分认识完毕。其实我们只做了粗浅的介绍，真正实用中，还有大量的细分和类型设计。除了少数我们介绍的经典如 MAE、MSE 等，每一个新的损失函数，都可能意味着有自己独特的配套神经网络结构。
究其原因，还是在于损失函数作用的范围，在于衡量整个网络的迭代，这决定了它不太可能会脱离而存在。使用中，需要小心。
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从这个例子就能看出， 有效的工程化能够极大提升算法的训练效率，减小耗时。
这即是工程师在此处的关键作用。
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不过，在起步阶段，我们还需要决定具体使用哪一种模型类型，来构建面向目标的神经网络。可供选择的类型，其实在本章的开篇就已介绍，即深度神经网络（DNN [Deep Neural Network]）的分类（见 4.1
这里我们主要对 当下主流的 CNN、RNN、GAN、Transformer 类别 ，进行说明。
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相信未来，我们仍然能够一睹 CNN 回归 LLM 多模态语言大模型的风采。
至此，CNN 的初级概念和网络结构，基本介绍完毕。有了这些知识背景，在了解 CNN 的各种类型网络的设计时，亦能窥得大概。其余就需要仔细钻研论文，以了解全貌了。
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如果 RNN 在短期内没有进一步突破，可见 Transformer 会逐步取而代之。但这，并不意味着 RNN 会退出历史舞台。技术永远都是博弈的过程，在人工智能的终极命题被解决前，无人能够断言。
需要注意的是，RNN 从始至终意图解决的都是“记忆”问题，而非 CNN 所解决的“提取”问题 。两者 并不冲突 ，甚至还可以适度融合，即组合形成 CNN+RNN 融合模型（Hybrid Model）。由 CNN 的特征提取（FE）子网得倒高级特征，再经过 RNN 代替原 CNN 的特征选择（FS）子网和结果输出（RO）子网，实现对高级特征的时间敏感训练。
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[22] Bahdanau D, Cho K, Bengio Y. Neural machine translation by jointly learning to align and translate[J]. arXiv preprint arXiv:1409.0473, 2014.
[23] Vaswani A, Shazeer N, Parmar N, et al. Attention is all you need[J]. Advances in neural information processing systems, 2017, 30.
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在开始搭建分析环境之前，还需要对常用的工具库进行简单的介绍。由于分析所采用的工程手段，多为以 Python 为脚本语言编写的简单处理流，因此，我们需要使用到的基本库，皆为 Python 工具库。
于是为方便后续索引、使用、总结，从库功能性上做简单归类，可以分为：常用数学库 、视频分析库 和 音频分析库 。
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光流计算：
-calcOpticalFlowFarneback （HS 法）,
-calcOpticalFlowPyrLK （LK 法）
+calcOpticalFlowFarneback （HS 法 calcOpticalFlowPyrLK （LK 法 运动检测：
CamShift ,
meanShift
@@ -2688,7 +2675,7 @@ 其次，是扩展库（opencv-contrib-python）所包含的额外模块。
扩展库涵盖了较多 传统计算机视觉（CV）高级算法 ，部分使用配参会较核心库更为复杂。同时，其中涉及 3D 匹配 的功能，大部分会用到 空间位姿计算（Spatial Posture Calculation） 来表示物体 在场景中的定位情况 。而对于此类涉及具有实际意义 3D 场景或物体的算法，想要展示其处理结果，一般都需要用构建空间化的渲染管线完成 ，而无法再直接使用 Matplotlib 做快速绘制（除非引入外部位姿库，或自实现）。介于此，有关 3D 绘制的部分，我们于未来再行讨论。
现在，让我们来看都有哪些 功能扩展 。
-生物识别扩展模块（cv2.bioinspired）的常用函数（简，仅列出名称），用于感知模拟（重要）：
+生物识别扩展模块（cv2.bioinspired）的常用函数（简），用于感知模拟（重要）：
视网膜模型（需 opencv-contrib-python 扩展的 cv2.bioinspired_Retina 模块），通过（cv2.）bioinspired_Retina.create 创建实例：
<Retina> 视网膜模拟类型实例
-表面检测点对特征匹配（PPF）扩展模块（cv2.ppf_match_3d）的常用函数（简，仅列出名称）：
+表面检测点对特征匹配（PPF）扩展模块（cv2.ppf_match_3d）的常用函数，简：
点云模型（需 opencv-contrib-python 扩展的 cv2.ppf_match_3d 模块），通过（cv2.） ppf_match_3d.loadPLYSimple 加载 多边形点云格式（PLY [Polygon File Format]）文件（.ply） ，来创建点云模型实例：
<Mat> 模型被加载 PLY 文件的光栅化与法线等信息，以 OpenCV 的 Mat 格式储存<Entity>.forward
-GPU 加速扩展模块（cv2.cuda）的常用函数，是同名基础模块算法 CUDA 加速版，仅列出名称：
+GPU 加速扩展模块（cv2.cuda）的常用函数，是同名基础模块算法 CUDA 加速版，简：
GPU 信息：
cuda.getCudaEnabledDeviceCount ,
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+ 5.1.4 其他分析软件
@@ -2178,7 +2165,7 @@ ө
[9] Bradski, Gary, and Adrian Kaehler. "OpenCV." Dr. Dobb’s journal of software tools 3.2 (2000).
[10] P. J. Besl and N. D. McKay, "A method for registration of 3-D shapes," in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 14, no. 2, pp. 239-256, Feb. 1992, doi: 10.1109/34.121791.
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Best Wish！💗
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5.1.1 常用数学库（Numpy、Pandas、Mateplotlib） 5.1.2 音频分析库（SoundFile、PyAudio、Librosa、Aubio） 5.1.3 视频分析库（PyOpenCV、Color-Science） 5.1.4 分析环境准备 5.1.5 其他分析软件 5.1.4 其他分析软件
【参考文献】 音࢜
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