数字信号处理外文翻译

毕业设计(论文)外文资料翻译

专业：自动化

姓名：

学号：

外文题目：The Breadth and Depth of DSP

外文出处：The Scientist and Engineer's Guide

to DSP

1 DSP的广度和深度

数字信号处理是最强大的技术，将塑造二十一世纪的科学与工程之一。革命性的变化已经在广泛的领域：通信，医疗成像，雷达和声纳，高保真音乐再现，石油勘探，仅举几例。上述各领域已建立了深厚的DSP技术，用自己的算法，数学，和专门技术。这种呼吸和深度的结合，使得它不可能为任何一个人掌握所有已开发的DSP技术。 DSP教育包含两个任务：学习一般适用于作为一个整体领域的概念，并学习您感兴趣的特定领域的专门技术。本章开始描述DSP已在几个不同领域的戏剧性效果的数字信号处理的世界，我们的旅程。革命已经开始。1.1 DSP的根源

独特的数据类型，它使用的信号，数字信号处理是区别于其他计算机科学领域。在大多数情况下，这些信号源于感觉来自现实世界的数据：地震的震动，视觉图像，声波等DSP是数学，算法，并用来操纵这些信号的技术后，他们已被转换成数字形式。这包括了各种目标，如：加强视觉图像识别和语音生成，存储和传输的数据压缩，等假设我们重视计算机模拟 - 数字转换器，并用它来获得一个现实世界的数据块。 DSP回答了这个问题：下一步怎么办？

DSP的根是在20世纪60年代和70年代数字计算机时首次面世。电脑是昂贵的，在这个时代，DSP是有限的，只有少数关键应用。努力开拓，在四个关键领域：雷达和声纳，国家安全风险是石油勘探，可以大量资金;太空探索，其中的数据是不可替代的;和医疗成像，可节省生活。 20世纪80年代和90年代的个人电脑革命，引起新的应用DSP的爆炸。而不是由军方和政府的需求动机，DSP 的突然被带动的商业市场。任何人士如认为他们可以使资金在迅速扩大的领域突然一个DSP供应商。 DSP的市民等产品达到：移动电话机，光盘播放器，电子语音邮件。

这一技术革命，从自上而下的发生。在20世纪80年代初，DSP是研究生水平的课程，在电气工程教授。十年后，DSP已成为标准的本科课程的一部分。今天，DSP是一种在许多领域的科学家和工程师所需要的基本技能。作为一个比喻，DSP可以比以前的技术革命：电子。虽然仍是电气工程领域，几乎所有的科学家和工程师有一些基本的电路设计的背景。没有它，他们将失去在科技世界。 DSP 具有相同的未来。

这最近的历史是超过了好奇，它有一个巨大的影响你的学习能力和使用DSP。假设你遇到一个DSP的问题，并把课本或其他出版物，以找到一个解决方

案。你通常会发现什么是页后页方程，模糊的数学符号，不熟悉的术语。这是一场恶梦！ DSP的文献多是令人费解，甚至在该领域经验丰富的。这并不是说有什么错用这种材料，它只是一个非常特殊的观众。国家的最先进的研究人员需要这种详细的数学理解的工作的理论意义。

这本书的一个基本前提是，可以学到最实用的DSP技术，并没有详细的数学和理论的传统障碍。科学家和工程师的数字信号处理指南是写给那些想要使用DSP作为一种工具，而不是一个新的职业生涯。

本章的其余部分说明，其中DSP已经产生了革命性的变化的地区。当你通过每个应用程序，请注意，DSP是非常跨学科，依托在许多相邻领域的技术工作。正如图。如果你想专注于DSP，这是多领域，则还需要研究。

1.2 通信

通信是信息传输从一个位置到另一个。这包括各种形式的信息：电话交谈，电视信号，计算机中的文件，和其他类型的数据。传输信息，你需要在两个地点之间的通道。这可能是一个线对无线电信号，光纤等电信公司接收他们的客户的信息转移支付，而他们一定要以建立和维护渠道。金融的底线很简单：信息越多，他们可以通过一个单一的通道，他们更多的钱。 DSP已彻底改变电信业在许多领域：信号音的产生和检测，频带的转移，过滤，除去电源线的嗡嗡声，从电话网络等具体的例子将在这里讨论：复用，压缩和回声控制。

1.2.1 复用

在世界上大约有10亿电话。在按几个按钮，开关网络允许其中任何一项，只有几秒钟的任何其他连接。这项任务的艰巨，是超乎想象！直到20世纪60年代，两个电话之间的连接需要通过机械开关和放大器的模拟语音信号。一个连接需要一对导线。相比之下，DSP音频信号转换成串行数字数据流。由于位可以轻松地交织在一起，后来分开，很多电话交谈可以传输渠道单一。例如，一个电话标准，被称为T载波系统可以同时传送24个语音信号。每个语音信号进行采样，每秒8000次，使用一个8位集成的（对数压缩）模拟到数字的转换。这个结果在64,000比特/秒，所有24个被包含在1.544兆比特/秒的渠道代表每个语音信号。这个信号可以传输，使用普通电话线，22号铜线，一个典型的互连距离约6000英尺。数字传输的资金优势是巨大的。线和模拟开关是昂贵的数字逻辑门价格便宜。

1.2.2 压缩

当语音信号数字化，在8000样本/秒，大多数的数字信息是多余的。也就是说，任何一个样本进行信息主要由邻近的样品重复。 DSP算法已发展到几十个数字化语音信号转换成数据流，需要较少的比特/秒。这些被称为数据压缩算法。匹配解压缩算法，用于恢复其原来的形式的信号。这些算法不同的金额达到压缩和音质。在一般情况下，减少64千比特/秒的数据传输速率为32千比特/秒的结果，在不损失音质。当压缩到8千比特/秒的数据传输速率，声音明显受到影响，但仍然可用的长途电话网络。达到的最高压缩约2千比特/秒，高度扭曲的声音，但可用于某些应用，如军事和海底通信。

1.2.3 回声

控制回声是一个严重的问题，在长途电话连接。当你走进一个电话，你的声音信号传播连接的接收器，它的一部分返回的回声。如果连接是几百公里内，接收回声所用的时间只有几毫秒。人类的耳朵习惯于听到这些小的时间延迟的回声，连接听起来很正常。随着距离变大，回声变得越来越明显和刺激性。延迟是几百毫秒洲际通信，特别是反感。数字信号处理攻击这类型的问题，通过测量返回信号，并产生适当的反信号取消违规回声。同样的技术，允许免提电话用户听取和不战而音频反馈（啸）在同一时间发言。它也可用于减少环境噪声，取消它与数字产生抗噪。

1.3 音频处理

主要的两个人的感官是视觉和听觉。相应地，许多DSP的有关图像和音频处理。人们听音乐和语音。 DSP已经在这两个领域取得了革命性的变化。

1.3.1 音乐

从音乐家的麦克风，高保真的扬声器的路径是相当长。数字数据表示，重要的是要防止通常与模拟存储和操作相关的退化。这是非常熟悉的人与光盘，录音带的音乐素质。在一个典型的场景，音乐作品在多个频道或曲目的录音室录制。在某些情况下，这甚至涉及个别乐器和歌手分别记录。这样做是为了给录音师更大的灵活性，创造的最终产品。被称为复杂的过程，结合到最终产品的个别曲目的缩混。 DSP可以在组合提供几个重要的功能，包括：过滤，加法和减法信号，信号的编辑，等等。

最有趣的音乐准备的DSP应用之一是人工混响。如果各个渠道的简单相加，导致一块听起来体弱及摊薄，音乐家多，如果在户外玩耍。这是因为听众都深受影响的音乐，通常是在录音室最小的回声或混响内容。 DSP允许人造回声和混

响加在混合模拟各种理想的听音环境。几百毫秒延迟的回声，给像位置的大教堂的印象。 10-20毫秒的延迟添加回声提供更多的适度规模聆听室的看法。

1.3.2 语音生成

语音生成和识别被用于人类和机器之间的沟通。而不是用你的双手和眼睛，你用你的嘴和耳朵。当你的手和眼睛应做别的东西，如：驾驶汽车，进行手术，或不幸敌人发射你的武器，这是非常方便。两种方法用于计算机生成的讲话：数码录音和声道模拟。在数码录音，一个人的扬声器的声音数字化处理和储存，通常在压缩形式。在播放过程中，存储的数据压缩和转换成模拟信号。整个小时的录音讲话要求只有约3兆字节的存储空间，即使是小规模的计算机系统内的功能。这是今天使用的数字语音代最常用的方法。

声道模拟器比较复杂，试图模仿人类创造讲话的物理机制。人类声道是声腔与商会的大小和形状确定的共振频率。声音源于声道声和摩擦音，在两种基本方式之一。浊音，声带振动产生周期脉冲附近的空气进入声乐腔。相比之下，摩擦音源于在嘈杂的空气湍流，如牙齿和嘴唇，窄缢。声道模拟器操作产生类似于激发这两种类型的数字信号。共鸣腔的特点是通过类似共振的激励信号，通过数字滤波器的模拟。这种方法是在一个非常早期的DSP成功故事，讲拼写，广泛销售的儿童电子学习援助。

1.3.3 语音识别

人类语音的自动识别是非常多讲话一代困难。语音识别是一个经典的东西，人类的大脑好例子，但数码电脑做的很差。数码电脑可以存储和调用大量数据，在炽烈速度执行数学计算，并没有变得无聊或低效重复的任务。不幸的是，现今电脑执行得非常糟糕时，面临着与原始的感官数据。教学计算机发送给您每月的电费是很容易的。在同一台计算机教学，以了解你的声音，是一大创举。

数字信号处理一般接近语音识别的问题，在两个步骤：特征提取，特征匹配。传入的音频信号中的每个单词是孤立的，然后分析激发和共鸣频率识别的类型。这些参数与前面的例子中，找出最接近的说话。通常情况下，这些系统只有几百字的限制，只能接受具有鲜明的字与字之间的停顿的讲话，以及必须为各扬声器培训。虽然这是许多商业应用提供足够的，这些限制是震撼人心相比，人类的听觉能力。有巨大的财政奖励那些生产成功的商业产品，要在这方面做的大量工作。

1.4 回声定位

一个常用的方法是获得远程对象的信息，超生波的关闭。例如，雷达通过发

射无线电波脉冲，并从飞机回声检查接收到的信号。声纳，通过水传播的声波探测潜艇和其他水下物体。地球物理学家已经长探测地球所设置的爆炸和听回声从岩石层深埋。虽然这些应用都有一个共同的线程，每个人都有自己的具体问题和需求。数字信号处理，在所有这三个领域产生革命性的变化。

1.4.1 雷达

雷达是无线电探测和测距的缩写。在最简单的雷达系统，无线电发射机产生的无线电频率能量的脉冲长几微秒。此脉冲被送入一个高度定向天线，以光的速度在产生的无线电波传播距离。飞机在这一波的路径将反映能源回向接收天线的一小部分，位于附近的传输站点。脉冲传输和接收到的回波之间的运行时间从计算到物体的距离。发现对象的方向更简单地说，你知道你指出的定向天线时收到回音。

经营范围的雷达系统是由两个参数决定：多少能源是在初始脉冲，无线电接收机的噪声水平。不幸的是，增加脉冲能量通常需要较长的脉搏。反过来，在较长的脉冲减少经过时间的测量准确度和精密度。这两个重要参数之间的冲突结果：能够在远距离探测的对象，并能准确地判断一个对象的距离。

DSP具有革命性的雷达三个方面，所有这些都涉及到这个基本问题。首先，DSP可以压缩后收到的脉冲，提供更好的距离决心，没有减少的经营范围。其次，DSP可以过滤接收到的信号，以减少噪音。这扩大了范围，不降解的距离的决心。第三，DSP能够快速选择不同的脉冲形状和长度和发电。除其他事项外，这使得脉冲为一个特定的检测问题进行了优化。现在这么多令人印象深刻的部分：是在采样率可比使用的无线电频率，高达几百兆赫的！当谈到雷达，DSP硬件设计高速，因为它是算法。

1.4.2 声纳

声纳是一个声音导航和测距的缩写。它分为两大类，主动和被动。在主动声纳，声脉冲在2 kHz和40 kHz之间传送入水，和由此产生的回声的检测和分析。主动声纳的用途包括：海底机构的检测与定位，导航，通信，测绘海底。 10至100公里的一个最大的经营范围是典型的。相比之下，被动声纳根本监听水下声音，其中包括：自然动荡，海洋生物，从潜艇和水面舰艇的机械声音。由于被动声纳发出能量，它是秘密行动的理想选择。你要检测的其他人，没有他侦测你。被动声纳最重要的应用是在军事监视系统，探测和跟踪潜艇。被动声纳通常使用较低的频率比主动声纳，因为他们通过吸收少水传播。检测范围可以是数千公里。

DSP已彻底改变了许多在同一地区的雷达声纳：脉冲发生器，脉冲压缩，过滤检测到的信号。有一种观点认为，声纳比雷达简单，因为涉及频率较低。另一种观点认为，声纳是比雷达更困难，因为环境是少得多的统一和稳定。声纳系统通常采用的发送和接收的元素，而不仅仅是一个单一渠道广泛的阵列。通过适当控制和混合信号，在这些众多的元素，可以避开声纳系统发射脉冲到所需的位置，并确定回声收到的方向。为了处理这些多渠道，声纳系统需要作为雷达的同样庞大的运算能力。

1.4.3 反射地震

早在20世纪20年代，地球物理学家发现，声音探测地壳结构可以。勘探者可能掀起爆炸和记录边界层超过地表以下10公里的回声。这些回声地震解释由原始的眼睛映射地下结构。地震反射法迅速成为主要方法，为寻找石油和矿藏，今天依然如此。

在理想的情况下，到地面发出的声音脉冲产生一个单脉冲穿过每个边界层的回波。不幸的是，情况通常不是这么简单。每个回波返回到表面，必须通过所有其他边界层以上，它起源。这可能会导致在层与层之间的弹跳回声，从而导致在表面上被检测的回声的回声。这些二次相呼应，可以检测到的信号非常复杂和难以解释的。数字信号处理技术已广泛应用于自1960年以来，隔离从二次回波反射地震主。早期地球物理学家怎么没有DSP管理？答案很简单：他们期待在方便的地方，多次反射最小化。 DSP的允许，在困难的地方，如大海，发现油。1.5 影像处理

图像信号特色。首先，他们是一个空间（距离）超过参数的措施，而大多数信号是随着时间的推移参数的措施。第二，它们包含了大量的信息。例如，超过10兆字节，可存储一秒钟的电视录像。这是一千倍以上，比类似长度的语音信号。第三，质量的最终判断往往是人类的主观评价，而不是一个客观的标准。这些特色使图像处理DSP内部的不同分组。

1.5.1 医疗

在1895年，威廉·康拉德伦琴发现X射线可以通过大量的问题。医学革新的能力，看里面的活生生的人体。医用X射线系统在仅仅几年在世界各地传播。尽管有其明显的成功，医用X射线成像由四个方面的问题是有限的，直到DSP 及相关技术在20世纪70年代。首先，在体内的重叠结构可以躲在彼此。例如，心脏部分可能不可见背后的肋骨。其次，它并不总是能够区分类似的组织。例如，

它可能是能够从软组织中分离出来的骨头，但不能区分肝肿瘤。第三，X射线图像表明，人体的解剖结构，而不是生理，身体的运作。长相酷似一个死一个的X 射线图像的X射线图像，一个活生生的人！四，X射线照射可引起癌症，要求它被只用谨慎和适当的理由。

在1971年引进的第一个电脑断层扫描仪（以前称为计算机轴向断层扫描，或CAT扫描仪）的重叠结构的问题得到解决。计算机断层扫描（CT）是数字信号处理的一个典型的例子。病人的身体正在审议的部分，通过X射线从多个方向。，而不是简单的形成与检测到的X射线图像信号转换成数字数据，并存储在计算机中。然后使用这些信息来计算，似乎是通过人体切片图像。这些图像表明远远大于传统的技术细节，从而显着提高诊断和治疗。在CT的影响是几乎一样大的X 射线成像本身的原始介绍。短短几年，在世界的每一个大医院有一个CT扫描仪的访问。 1979年，两个CT的原则贡献者，戈弗雷北路亨斯菲尔德和艾伦研究科马克，分享诺贝尔医学奖。这是DSP的好！

过去三年X射线的问题已经解决了用比其他X射线，如无线电和声波穿透能量。 DSP在所有这些技术中起着关键作用。例如，磁共振成像（MRI）利用磁场与无线电波探测人体内部。适当调整的强度和频率等领域引起原子核中的量子能态之间的身体产生共鸣的局部地区。在发射天线放在身体附近发现一所中学的无线电波，这种共振的结果。这个检测信号的强度和其他特性提供关于在共振的局部地区的信息。磁场的调整，使整个身体的共振区域进行扫描，映射的内部结构。这种信息通常是图像，就像在电脑断层扫描。除了提供不同类型的软组织之间的优秀歧视，MRI可提供生理信息，如血流量，通过动脉。磁共振完全依赖数字信号处理技术，并没有他们不能实施。

1.5.2 空间

有时候，你只是做了一个不好的图片最。这通常是从无人驾驶的卫星和空间探测车拍摄的图像的情况下。没有人会派修理工到火星只需要相机调整旋钮！DSP可以改善极为不利的条件，在几个方面下拍摄的图像质量：亮度和对比度调整，边缘检测，降噪，重点调整，运动模糊减少，等有空间扭曲的图像，如时遇到的是一个平面图像一个球形的星球，也可以扭曲成一个正确的表示。许多单个图像也可以被组合成一个单一的数据库，允许以独特的方式显示信息。例如，在一个遥远的星球表面的空中飞行模拟视频序列。

1.5.3 商业影像产品

在图像信息的内容质量数量向公众出售的系统是一个问题。商业系统必须是廉价的，这并不网格与大容量的存储器和高数据传输速率。这一困境的一个答案是图像压缩。正如语音信号，图像包含大量的冗余信息，并可以通过算法，减少了代表他们所需要的数位运行。电视和其他运动图像压缩尤其适合，因为大多数的图像仍然从帧到帧相同。商业影像产品，利用这种技术的优点包括：视频电话，计算机程序，显示移动的图片和数字电视。

1 The Breadth and Depth of DSP

Digital Signal Processing is one of the most powerful technologies that will shape science and engineering in the twenty-first century. Revolutionary changes have already been made in a broad range of fields: communications, medical imaging, radar & sonar, high fidelity music reproduction, and oil prospecting, to name just a few. Each of these areas has developed a deep DSP technology, with its own algorithms, mathematics, and specialized techniques. This combination of breath and depth makes it impossible for any one individual to master all of the DSP technology that has been developed. DSP education involves two tasks: learning general concepts that apply to the field as a whole, and learning specialized techniques for your particular area of interest. This chapter starts our journey into the world of Digital Signal Processing by describing the dramatic effect that DSP has made in several diverse fields. The revolution has begun.

1.1 The Roots of DSP

Digital Signal Processing is distinguished from other areas in computer science by the unique type of data it uses: signals. In most cases, these signals originate as sensory data from the real world: seismic vibrations, visual images, sound waves, etc. DSP is the mathematics, the algorithms, and the techniques used to manipulate these signals after they have been converted into a digital form. This includes a wide variety of goals, such as: enhancement of visual images, recognition and generation of speech, compression of data for storage and transmission, etc. Suppose we attach an analog-to-digital converter to a computer and use it to acquire a chunk of real world data. DSP answers the question: What next?

The roots of DSP are in the 1960s and 1970s when digital computers first became available. Computers were expensive during this era, and DSP was limited to only a few critical applications. Pioneering efforts were made in four key areas: radar & sonar, where national security was at risk; oil exploration, where large amounts of money could be made; space exploration, where the data are irreplaceable; and medical imaging, where lives could be saved. The personal computer revolution of the 1980s and 1990s caused DSP to explode with new applications. Rather than being motivated by military and government needs, DSP was suddenly driven by the

commercial marketplace. Anyone who thought they could make money in the rapidly expanding field was suddenly a DSP vendor. DSP reached the public in such products as: mobile telephones, compact disc players, and electronic voice mail. Figure 1-1 illustrates a few of these varied applications.

This technological revolution occurred from the top-down. In the early 1980s, DSP was taught as a graduate level course in electrical engineering. A decade later, DSP had become a standard part of the undergraduate curriculum. Today, DSP is a basic skill needed by scientists and engineers in many fields. As an analogy, DSP can be compared to a previous technological revolution: electronics. While still the realm of electrical engineering, nearly every scientist and engineer has some background in basic circuit design. Without it, they would be lost in the technological world. DSP has the same future.

This recent history is more than a curiosity; it has a tremendous impact on your ability to learn and use DSP. Suppose you encounter a DSP problem, and turn to textbooks or other publications to find a solution. What you will typically find is page after page of equations, obscure mathematical symbols, and unfamiliar terminology. It's a nightmare! Much of the DSP literature is baffling even to those experienced in the field. It's not that there is anything wrong with this material, it is just intended for a very specialized audience.State-of-the-art researchers need this kind of detailed mathematics to understand the theoretical implications of the work.

A basic premise of this book is that most practical DSP techniques can be learned and used without the traditional barriers of detailed mathematics and theory. The Scientist and Engineer’s Guide to Digital Si gnal Processing is written for those who want to use DSP as a tool, not a new career.

The remainder of this chapter illustrates areas where DSP has produced revolutionary changes. As you go through each application, notice that DSP is very interdisciplinary, relying on the technical work in many adjacent fields. As Fig. 1-2 suggests, the borders between DSP and other technical disciplines are not sharp and well defined, but rather fuzzy and overlapping. If you want to specialize in DSP, these are the allied areas you will also need to study.

1.2 Telecommunications

Telecommunications is about transferring information from one location to another. This includes many forms of information: telephone conversations, television signals, computer files, and other types of data. To transfer the information, you need a channel between the two locations. This may be a wire pair, radio signal, optical fiber, etc. Telecommunications companies receive payment for transferring their customer's information, while they must pay to establish and maintain the channel. The financial bottom line is simple: the more information they can pass through a single channel, the more money they make. DSP has revolutionized the telecommunications industry in many areas: signaling tone generation and detection, frequency band shifting, filtering to remove power line hum, etc. Three specific examples from the telephone network will be discussed here: multiplexing, compression, and echo control.

1.2.1 Multiplexing

There are approximately one billion telephones in the world. At the press of a few buttons, switching networks allow any one of these to be connected to any other in only a few seconds. The immensity of this task is mind boggling! Until the 1960s, a connection between two telephones required passing the analog voice signals through mechanical switches and amplifiers. One connection required one pair of wires. In comparison, DSP converts audio signals into a stream of serial digital data. Since bits can be easily intertwined and later separated, many telephone conversations can be transmitted on a single channel. For example, a telephone standard known as the T-carrier system can simultaneously transmit 24 voice signals. Each voice signal is sampled 8000 times per second using an 8 bit companded (logarithmic compressed) analog-to-digital conversion. This results in each voice signal being represented as 64,000 bits/sec, and all 24 channels being contained in 1.544 megabits/sec. This signal can be transmitted about 6000 feet using ordinary telephone lines of 22 gauge copper wire, a typical interconnection distance. The financial advantage of digital transmission is enormous. Wire and analog switches are expensive; digital logic gates are cheap.

1.2.2 Compression

When a voice signal is digitized at 8000 samples/sec, most of the digital information is redundant. That is, the information carried by any one sample is largely

duplicated by the neighboring samples. Dozens of DSP algorithms have been developed to convert digitized voice signals into data streams that require fewer bits/sec. These are called data compression algorithms. Matching un-compression algorithms are used to restore the signal to its original form. These algorithms vary in the amount of compression achieved and the resulting sound quality. In general, reduce the data rate from 64 kilobits/sec to 32 kilobits/sec results in no loss of sound quality. When compressed to a data rate of 8 kilobits/sec, the sound is noticeably affected, but still usable for long distance telephone networks. The highest achievable compression is about 2 kilobits/sec, resulting in sound that is highly distorted, but usable for some applications such as military and undersea communications.

1.2.3 Echo control

Echoes are a serious problem in long distance telephone connections. When you speak into a telephone, a signal representing your voice travels to the connecting receiver, where a portion of it returns as an echo. If the connection is within a few hundred miles, the elapsed time for receiving the echo is only a few milliseconds. The human ear is accustomed to hearing echoes with these small time delays, and the connection sounds quite normal. As the distance becomes larger, the echo becomes increasingly noticeable and irritating. The delay can be several hundred milliseconds for intercontinental communications, and is particularly objectionable. Digital Signal Processing attacks this type of problem by measuring the returned signal and generating an appropriate anti-signal to cancel the offending echo. This same technique allows speakerphone users to hear and speak at the same time without fighting audio feedback (squealing). It can also be used to reduce environmental noise by canceling it with digitally generated anti-noise.

1.3 Audio Processing

The two principal human senses are vision and hearing. Correspondingly, much of DSP is related to image and audio processing. People listen to both music and speech. DSP has made revolutionary changes in both these areas.

1.3.1 Music

The path leading from the musician's microphone to the audiophile's speaker is remarkably long. Digital data representation is important to prevent the degradation

commonly associated with analog storage and manipulation. This is very familiar to anyone who has compared the musical quality of cassette tapes with compact disks. In a typical scenario, a musical piece is recorded in a sound studio on multiple channels or tracks. In some cases, this even involves recording individual instruments and singers separately. This is done to give the sound engineer greater flexibility in creating the final product. The complex process of combining the individual tracks into a final product is called mix down. DSP can provide several important functions during mix down, including: filtering, signal addition and subtraction, signal editing, etc.

One of the most interesting DSP applications in music preparation is artificial reverberation. If the individual channels are simply added together, the resulting piece sounds frail and diluted, much as if the musicians were playing outdoors. This is because listeners are greatly influenced by the echo or reverberation content of the music, which is usually minimized in the sound studio. DSP allows artificial echoes and reverberation to be added during mix down to simulate various ideal listening environments. Echoes with delays of a few hundred milliseconds give the impression of cathedral like locations. Adding echoes with delays of 10-20 milliseconds provide the perception of more modest size listening rooms.

1.3.2 Speech generation

Speech generation and recognition are used to communicate between humans and machines. Rather than using your hands and eyes, you use your mouth and ears. This is very convenient when your hands and eyes should be doing something else, such as: driving a car, performing surgery, or (unfortunately) firing your weapons at the enemy. Two approaches are used for computer generated speech: digital recording and vocal tract simulation. In digital recording, the voice of a human speaker is digitized and stored, usually in a compressed form. During playback, the stored data are uncompressed and converted back into an analog signal. An entire hour of recorded speech requires only about three megabytes of storage, well within the capabilities of even small computer systems. This is the most common method of digital speech generation used today.

V ocal tract simulators are more complicated, trying to mimic the physical

mechanisms by which humans create speech. The human vocal tract is an acoustic cavity with resonant frequencies determined by the size and shape of the chambers. Sound originates in the vocal tract in one of two basic ways, called voiced and fricative sounds. With voiced sounds, vocal cord vibration produces near periodic pulses of air into the vocal cavities. In comparison, fricative sounds originate from the noisy air turbulence at narrow constrictions, such as the teeth and lips. V ocal tract simulators operate by generating digital signals that resemble these two types of excitation. The characteristics of the resonate chamber are simulated by passing the excitation signal through a digital filter with similar resonances. This approach was used in one of the very early DSP success stories, the Speak & Spell, a widely sold electronic learning aid for children.

1.3.3 Speech recognition

The automated recognition of human speech is immensely more difficult than speech generation. Speech recognition is a classic example of things that the human brain does well, but digital computers do poorly. Digital computers can store and recall vast amounts of data, perform mathematical calculations at blazing speeds, and do repetitive tasks without becoming bored or inefficient. Unfortunately, present day computers perform very poorly when faced with raw sensory data. Teaching a computer to send you a monthly electric bill is easy. Teaching the same computer to understand your voice is a major undertaking.

Digital Signal Processing generally approaches the problem of voice recognition in two steps: feature extraction followed by feature matching. Each word in the incoming audio signal is isolated and then analyzed to identify the type of excitation and resonate frequencies. These parameters are then compared with previous examples of spoken words to identify the closest match. Often, these systems are limited to only a few hundred words; can only accept speech with distinct pauses between words; and must be retrained for each individual speaker. While this is adequate for many commercial applications, these limitations are humbling when compared to the abilities of human hearing. There is a great deal of work to be done in this area, with tremendous financial rewards for those that produce successful commercial products.

1.4 Echo Location

A common method of obtaining information about a remote object is to bounce a wave off of it. For example, radar operates by transmitting pulses of radio waves, and examining the received signal for echoes from aircraft. In sonar, sound waves are transmitted through the water to detect submarines and other submerged objects. Geophysicists have long probed the earth by setting off explosions and listening for the echoes from deeply buried layers of rock. While these applications have a common thread, each has its own specific problems and needs. Digital Signal Processing has produced revolutionary changes in all three areas.

1.4.1 Radar

Radar is an acronym for Radio Detection And Ranging. In the simplest radar system, a radio transmitter produces a pulse of radio frequency energy a few microseconds long. This pulse is fed into a highly directional antenna, where the resulting radio wave propagates away at the speed of light. Aircraft in the path of this wave will reflect a small portion of the energy back toward a receiving antenna, situated near the transmission site. The distance to the object is calculated from the elapsed time between the transmitted pulse and the received echo. The direction to the object is found more simply; you know where you pointed the directional antenna when the echo was received.

The operating range of a radar system is determined by two parameters: how much energy is in the initial pulse, and the noise level of the radio receiver. Unfortunately, increasing the energy in the pulse usually requires making the pulse longer. In turn, the longer pulse reduces the accuracy and precision of the elapsed time measurement. This results in a conflict between two important parameters: the ability to detect objects at long range, and the ability to accurately determine an object's distance. DSP has revolutionized radar in three areas, all of which relate to this basic problem. First, DSP can compress the pulse after it is received, providing better distance determination without reducing the operating range. Second, DSP can filter the received signal to decrease the noise. This increases the range, without degrading the distance determination. Third, DSP enables the rapid selection and generation of different pulse shapes and lengths. Among other things, this allows the pulse to be

optimized for a particular detection problem. Now the impressive part: much of this is done at a sampling rate comparable to the radio frequency used, as high as several hundred megahertz! When it comes to radar, DSP is as much about high-speed hardware design as it is about algorithms.

1.4.2 Sonar

Sonar is an acronym for Sound Navigation and Ranging. It is divided into two categories, active and passive. In active sonar, sound pulses between 2 kHz and 40 kHz are transmitted into the water, and the resulting echoes detected and analyzed. Uses of active sonar include: detection & localization of undersea bodies, navigation, communication, and mapping the sea floor. A maximum operating range of 10 to 100 kilometers is typical. In comparison, passive sonar simply listens to underwater sounds, which includes: natural turbulence, marine life, and mechanical sounds from submarines and surface vessels. Since passive sonar emits no energy, it is ideal for covert operations. Y ou want to detect the other guy, without him detecting you. The most important application of passive sonar is in military surveillance systems that detect and track submarines. Passive sonar typically uses lower frequencies than active sonar because they propagate through the water with less absorption. Detection ranges can be thousands of kilometers.

DSP has revolutionized sonar in many of the same areas as radar: pulse generation, pulse compression, and filtering of detected signals. In one view, sonar is simpler than radar because of the lower frequencies involved. In another view, sonar is more difficult than radar because the environment is much less uniform and stable. Sonar systems usually employ extensive arrays of transmitting and receiving elements, rather than just a single channel. By properly controlling and mixing the signals in these many elements, the sonar system can steer the emitted pulse to the desired location and determine the direction that echoes are received from. To handle these multiple channels, sonar systems require the same massive DSP computing power as radar.

1.4.3 Reflection seismology

As early as the 1920s, geophysicists discovered that the structure of the earth's crust could be probed with sound. Prospectors could set off an explosion and record the

echoes from boundary layers more than ten kilometers below the surface. These echo seismograms were interpreted by the raw eye to map the subsurface structure. The reflection seismic method rapidly became the primary method for locating petroleum and mineral deposits, and remains so today.

In the ideal case, a sound pulse sent into the ground produces a single echo for each boundary layer the pulse passes through. Unfortunately, the situation is not usually this simple. Each echo returning to the surface must pass through all the other boundary layers above where it originated. This can result in the echo bouncing between layers, giving rise to echoes of echoes being detected at the surface. These secondary echoes can make the detected signal very complicated and difficult to interpret. Digital Signal Processing has been widely used since the 1960s to isolate the primary from the secondary echoes in reflection seismograms. How did the early geophysicists manage without DSP? The answer is simple: they looked in easy places, where multiple reflections were minimized. DSP allows oil to be found in difficult locations, such as under the ocean.

1.5 Image Processing

Images are signals with special characteristics. First, they are a measure of a parameter over space (distance), while most signals are a measure of a parameter over time. Second, they contain a great deal of information. For example, more than 10 megabytes can be required to store one second of television video. This is more than a thousand times greater than for a similar length voice signal. Third, the final judge of quality is often a subjective human evaluation, rather than an objective criterion. These special characteristics have made image processing a distinct subgroup within DSP.

1.5.1 Medical

In 1895, Wilhelm Conrad R?ntgen discovered that x-rays could pass through substantial amounts of matter. Medicine was revolutionized by the ability to look inside the living human body. Medical x-ray systems spread throughout the world in only a few years. In spite of its obvious success, medical x-ray imaging was limited by four problems until DSP and related techniques came along in the 1970s. First, overlapping structures in the body can hide behind each other. For example, portions

of the heart might not be visible behind the ribs. Second, it is not always possible to distinguish between similar tissues. For example, it may be able to separate bone from soft tissue, but not distinguish a tumor from the liver. Third, x-ray images show anatomy, the body's structure, and not physiology, the body's operation. The x-ray image of a living person looks exactly like the x-ray image of a dead one! Fourth, x-ray exposure can cause cancer, requiring it to be used sparingly and only with proper justification.

The problem of overlapping structures was solved in 1971 with the introduction of the first computed tomography scanner (formerly called computed axial tomography, or CAT scanner). Computed tomography (CT) is a classic example of Digital Signal Processing. X-rays from many directions are passed through the section of the patient's body being examined. Instead of simply forming images with the detected x-rays, the signals are converted into digital data and stored in a computer. The information is then used to calculate images that appear to be slices through the body. These images show much greater detail than conventional techniques, allowing significantly better diagnosis and treatment. The impact of CT was nearly as large as the original introduction of x-ray imaging itself. Within only a few years, every major hospital in the world had access to a CT scanner. In 1979, two of CT's principle contributors, Godfrey N. Hounsfield and Allan M. Cormack, shared the Nobel Prize in Medicine. That's good DSP!

The last three x-ray problems have been solved by using penetrating energy other than x-rays, such as radio and sound waves. DSP plays a key role in all these techniques. For example, Magnetic Resonance Imaging (MRI) uses magnetic fields in conjunction with radio waves to probe the interior of the human body. Properly adjusting the strength and frequency of the fields cause the atomic nuclei in a localized region of the body to resonate between quantum energy states. This resonance results in the emission of a secondary radio wave, detected with an antenna placed near the body. The strength and other characteristics of this detected signal provide information about the localized region in resonance. Adjustment of the magnetic field allows the resonance region to be scanned throughout the body, mapping the internal structure. This information is usually presented as images, just as

in computed tomography. Besides providing excellent discrimination between different types of soft tissue, MRI can provide information about physiology, such as blood flow through arteries. MRI relies totally on Digital Signal Processing techniques, and could not be implemented without them.

1.5.2 Space

Sometimes, you just have to make the most out of a bad picture. This is frequently the case with images taken from unmanned satellites and space exploration vehicles. No one is going to send a repairman to Mars just to tweak the knobs on a camera! DSP can improve the quality of images taken under extremely unfavorable conditions in several ways: brightness and contrast adjustment, edge detection, noise reduction, focus adjustment, motion blur reduction, etc. Images that have spatial distortion, such as encountered when a flat image is taken of a spherical planet, can also be warped into a correct representation. Many individual images can also be combined into a single database, allowing the information to be displayed in unique ways. For example, a video sequence simulating an aerial flight over the surface of a distant planet.

1.5.3 Commercial Imaging Products

The large information content in images is a problem for systems sold in mass quantity to the general public. Commercial systems must be cheap, and this doesn't mesh well with large memories and high data transfer rates. One answer to this dilemma is image compression. Just as with voice signals, images contain a tremendous amount of redundant information, and can be run through algorithms that reduce the number of bits needed to represent them. Television and other moving pictures are especially suitable for compression, since most of the image remain the same from frame-to-frame. Commercial imaging products that take advantage of this technology include: video telephones, computer programs that display moving pictures, and digital television.