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罗兰的管弦乐音色表PC Instrument Name 描述1 Violin Warm Section 温暖的小提琴乐器组与强的颤动的2 Slow Violin Section 小提琴乐器组以缓慢的攻击。
3 Violin Section 小提琴乐器组以快速进攻。
4 Violin Section /Vsw 小提琴乐器组那交换对颤动的声音，当演奏以强的速度时。
5 Violins spiccato 小提琴乐器组演奏了跳弓。
6 Violins Pizzcato 小提琴乐器组演奏了拨奏。
7 Troemolo Violins 小提琴乐器组演奏与颤音。
8 Viola Warm Section 温暖的中提琴部分与强的颤动的。
9 Slow Viola Section 中提琴部分以缓慢的攻击。
10 Viola Section 中提琴部分以快速进攻。
11 Viola Section /Vsw 中提琴部分那交换对颤动的声音，当演奏以强的速度时。
12 Violas spiccato 中提琴部分演奏了跳弓。
13 Violas Pizzcato 中提琴部分演奏了拨奏。
14 Troemolo Violas 中提琴部分演奏与颤音。
15 Cello Warm Section 温暖的大提琴部分与强的颤动的。
16 Cello Section 大提琴部分以快速进攻。
17 Cello Section /Vsw 大提琴部分那交换对颤动的声音，当演奏以强的速度时。
18 Cellos spiccato 大提琴部分演奏了跳弓。
19 Cellos Pizzcato 大提琴部分演奏了拨奏。
20 Troemolo Cellos 大提琴部分演奏与颤音。
21 ContraBass Section 最低音部分。
22 ContraBasses spicc 最低音部分演奏了跳弓。
23 ContraBassesPizzcato 最低音部分演奏了拨奏。
24 Troem ContraBasses 最低音部分演奏与颤音。
waves 效果器全套中英文说对照表99个英文中文01.Prologue 12:42 加载方法02.API-2500 09:06 压缩处理03.API-550A 09:04 均衡处理04.API-550B 05:29 均衡处理05.API-560 03:20 均衡处理06.AudioTrack 09:50 多重效果07.C1 Comp 04:16 压缩处理08.C1 Comp-Gate 12:33 压缩+门限09.C1 Comp-Sc 18:00 侧链压缩10.C1 Gate 07:14 门限处理11.C4 13:14 多段动态12.DeEsser 03:44 齿音消除13.Doppler 04:48 声场调整14.Doubler 12:32 合唱效果15.Enigma 09:36 迷幻效果16.IDR 01:45 抖动处理17.L1-Ultramaximizer 02:54 母带处理18.L1-Ultramaximizer+ 03:49 母带处理19.L2 02:52 母带处理20.L3-MultiMaximizer 11:56 母带处理21.L3-UltraMaximizer 03:38 母带处理22.LinEq Broadband 05:40 均衡处理23.LinMB 08:57 多段动态24.MaxxBass 03:59 泛音处理25.MetaFlanger 06:40 镶边效果26.MondoMod 09:15 调制效果27.Morphoder 07:18 声码效果28.PAZ Analyzer 11:36 综合分析仪29.PAZ Frequency 15:31 频谱仪30.PAZ Meters 12:10 电平表31.PAZ Position 11:16 声场仪32.Qx-Paragraphic EQ 21:07 均衡处理33.RBass 05:22 泛音处理34.RChannel 09:23 多重效果35.RComp 18:11 压缩处理36.RDeEsser 04:32 齿音消除37.REQ bands 04:43 均衡处理38.RVerb 26:39 卷积混响39.RVox 04:00 压缩处理40.S1-Imager 07:23 立体声扩展41.S1-Shuffler 07:32 立体声扩展42.SoundShifter G Offline 08:11 变速变调43.SoundShifter P 03:26 变调处理44.SoundShifter P Offline 07:11 变速变调45.SSLChannel 36:49 通道条46.SSLComp 06:24 压缩处理47.SSLEQ 07:19 均衡处理48.SuperTap 24:43 延迟处理49.TransX 08:09 瞬间电平控制50.TrueVerb 19:11 模拟混响51.UltraPitch 07:55 自动和声52.X-Click 03:36 去咔哒声53.X-Crackle 03:40 去噼啪声54.X-Hum 06:13 滤波降噪55.X-Noise 10:45 采样降噪56.Z-Noise 12:24 采样降噪57.DeBreath 06:09 去呼吸声58.Tune 01 22:22 音高修正（上）59.Tune 02 16:11 音高修正（中）60.Tune 03 18:07 音高修正（下）ＡｕｄｉｏＴｒａｃｋ是ｗａｖｅｓ的通道条效果器，是一款均衡器／压缩器／门限器的组合Ｃ１包括四个，Ｃ１ｃｏｍｐ是单纯的压缩器，Ｃ１ｃｏｍｐｇａｔｅ是压缩／门限的组合，Ｃ１ＳＣ是旁链压缩器（应用于广播等场合），Ｃ１ｇａｔｅ是单纯的门限Ｃ４是ｗａｖｅｓ的著名多段动态处理器Ｄｅｓｓｅｒ是消除齿音效果器Ｄｏｐｐｌｅｒ是掠过音效器，多普勒效应嘛Ｄｏｕｂｌｅｒ是声音加倍效果器，做合唱合奏用的Ｅｎｇｉｍａ是迷幻音效效果器，它利用相位调制原理来产生各种稀奇古怪的效果ＸＩＤＲ是ｗａｖｅｓ自己开发的噪声整型／抖动算法，转换采样深度时用来减小数字背景随机噪声Ｌ１／Ｌ２／Ｌ３都是限制器，区别一个比一个猛，Ｌ１可以放在分轨作限制，Ｌ２、Ｌ３是母带用的。
OscilloscopeOscilloscope is a very extensive use of electronic measuring instruments. It is invisible to the naked eye can transform electrical signals into visible images, allowing people to study various electrical phenomena of the change process. Oscilloscope using narrow, high-speed electronic form by e-beam, playing in the coated fluorescent substance of the screen surface, you can create tiny points of light. Under the action of the measured signal, the electron beam is just like a pen nib, you can screen the surface depicts the instantaneous value of the measured signal curves. Oscilloscope can be observed using a variety of signal amplitude versus time waveform curve, you can also use it to test a variety of power, such as voltage, current, frequency, phase, transfer rate and so on.An ordinary oscilloscope There are five basic components: display circuit, the vertical (Y axis) amplifier circuit, the horizontal (X axis) amplifier circuit, scanning and synchronous circuits, power supply circuits.The basic principle of waveform displayThe principle by the CRT we can see that a DC voltage added to a pair of deflection on the board, will spot the screen to generate a fixed displacement, the displacement increases with the size of the DC voltage proportional to. If the two DC voltages, respectively, at the same time added to the two pairs of vertical and horizontal deflection board, then the screen on the spot location of the displacement in both directions by the joint decision.In order to screen the graphics on the stability of the frequency of the measured signal voltage should be the frequency of sawtooth voltage to maintain the relationship between the integer ratio, that is, synchronization relationship. In order to achieve this, requires the frequency sawtooth voltage is continuously adjustable to adapt to observe the periodic signals of different frequencies. Second, because the measured signal frequency and the sawtooth oscillation signal frequency is relatively unstable, even if the frequency of the sawtooth voltage and measured the temporary redeployment of an integer multiple of the relationship between signal frequency does not make graphics have remained stable.Therefore, the oscilloscope are equipped with synchronizer. That is, a certain part of the circuit in the sawtooth with a sync signal to induce the synchronous scan for only produce a continuous scan (that generate a continuous cycle of sawtooth) a state of simple oscilloscope (such as the type made SB-10 Oscilloscope , etc.), the need to enter in its sweep circuit has been observed with a frequency related to the synchronization signal, which increases the frequency of sync near the sawtooth frequency self-oscillation frequency (or close to integer multiple of), you can zigzag wave frequency "drag synchronization" or"lock." For those who waiting to be scanned (ie, usually does not produce sawtooth, as measured when the signal to generate a sawtooth wave to conduct a scan) function of the oscilloscope (such as domestic ST-16-type oscilloscope, SBT-5 synchronous oscilloscope, SR-8 dual trace oscilloscope, etc.), the need to enter one of its scanning circuit associated with the measured signal trigger signals, so that the scanning process in close cooperation with the measured signal. Thus, as long as needed to select the appropriate sync signal or trigger signal, can make any wish to study the process to keep pace with the sawtooth scanning frequency.Two-lane, dual-trace oscilloscope display of PrinciplePractice technology in the electronic process, is often necessary while observing the two (or more than two) signals with time-varying process. These different signals and electrical parameter testing and comparison. In order to achieve this goal, it is in the application based on the principle of an ordinary oscilloscope, using the following two methods simultaneously display multiple waveforms: a double-line (or lines) oscillometric method; the other is the double-trace (orMulti-trace) oscillometric method. Application of these two methods created by the oscilloscope are known as two-wire (or line) and dual-trace oscilloscope (or trace) oscilloscope.Two-wire (or line) oscilloscope is used Spear (or gun) CRT to achieve. The following example in order to Spear CRT to be a brief explanation. Spear There are two mutually independent CRT electron gun produces two beam electron. Another two mutually independent deflection system, they are a bunch of their respective control electronics for the upper and lower, left and right movement. Screen are shared, and thus can simultaneously screen shows two different signal waveforms, two-lane single-shot oscilloscope can also be used to achieve the two-CRT. This is only a CRT electron gun, at work on a special electrode is divided into two beams of electronics. Then, from the two groups independent of each other tube deflection systems, the control of two-beam electron up and down, left and right movement. Screen is shared, it could also show two different signal waveforms. As the two-CRT manufacturing process requires high cost is also high, so applications are not very common.Dual-trace (or trace) oscilloscopeDual-trace (or trace) oscilloscope oscilloscope in the single-line, based on the addition of a dedicated electronic switch, use it to achieve the two (or more) difference between the waveform display. As the realization of two-trace (or trace) oscilloscope than the realization of two-wire (or line) is more simple oscilloscope, without the use of complex, expensive "dual-chamber" or "multi-cavity" CRT, so double-trace (or trace) oscilloscope to obtain a general application.Oscilloscope Application(A) Voltage measurementAny measurements made using the oscilloscope, are attributed to the voltage measurements. Oscilloscope can measure the voltage of the waveform amplitude, both can be measured DC voltage and sinusoidal voltage, but also can measure the pulse or non-sinusoidal voltage range. More useful is that it can measure the various parts of a pulse voltage waveform of the voltage amplitude, as indicated in the amount of impulse or top down and so on. This is any other voltage measurement instruments can not be compared.Direct measurementThe so-called direct measurement method, that is, planning based on expenditure directly from the measured voltage waveform on the screen height, and then converted into a voltage value. Quantitative test voltage, generally, in the Y-axis sensitivity of the fine-tuning knob switches go to the "calibrate" position, so that you can from the "V / div" instruction value and the measured signal seize direct calculation of the vertical axis coordinate value of the measured voltage value. Therefore, direct measurement method is also known as the ruler France.Direct measurement method is simple, but the error is greater. Factors of error reading error, parallax, and oscilloscope system error (attenuator, deflection systems, CRT edge effects) and so on.Comparative MeasurementComparative measurements is to use a known standard voltage waveform and comparing the measured voltage waveform measured voltage value obtained.The measured voltage Vx input Y-axis of the oscilloscope channels to regulate the Y-axis sensitivity selector switch "V / div" and thefine-tuning knob, so that screen shows easy to measure the height of Hx, and make a record, and "V / div" switch and fine-tuning knob position remains unchanged. Get rid of the measured voltage, adjustable to a known standard voltage Vs input Y-axis, adjusting the standard voltage output range, so that it displays the measured voltage of the same magnitude. At this point, the standard voltage range is equal to the measured output voltage range. Comparison measuring voltage can be avoided and errors caused by vertical systems, thereby enhancing measurement accuracy.(B) Time MeasurementOscilloscope time base and time can produce a linear relationship between the scan lines, and therefore can scale to measure the level of screen time waveform parameters, such as the cyclical repetition of the signal cycle, pulse width, time interval, rise time (leading edge) and fall time (trailing edge), two signal time difference and so on. The oscilloscope sweep speed switch "t / div" of "fine tuning" devices go to the calibration position, the displayed waveform in the horizontal direction represents the time scale can "t / div" switch the value ofdirect reading instruction computing, and thus more and accurately calculate the time parameters of the measured signal.(C) the phase measurementsOscilloscope measurements using the phase difference between the two sinusoidal voltages of practical significance can be measured with the counter frequency and time, but can not directly measure the phase relationship between the sinusoidal voltage. Phase measurement using the oscilloscope are many ways:Dual-trace methodLissajous Figures Measuring Phase(D) Frequency of measurementMeasuring signal frequency with the oscilloscope many ways, there are two basic methods.CycleFor any periodic signal can be used the aforementioned time interval measurement, determined before each cycle of time T, then the following formula derived frequency f: f = 1 / TLissajous Figures Measuring frequencyLissajous Figures measurement of frequency is quite accurate, but more time-consuming operation. At the same time, it applies only to measure the lower frequency signal.Oscilloscope trend in the development of fiveTechnology is changing the oscilloscope latest applications are endless. Oscilloscope manufacturers must closely follow the trend of new applications, designed to meet the specific needs of the user oscilloscope and software applications. This article will appear for the oscilloscope market, five trends analysis.Trend 1: From the parallel to serial measurement of measuring development Over the past embedded design often parallel architecture, which means that each component has its own bus path. Therefore, as long as you can use a pattern trigger or condition to trigger the events to find a sense of love, you can visually decode the data on the bus.However, modern embedded designs generally use a serial architecture- that is, to send a continuous bus data. The reason for this is that it requires less board space, lower cost, and using embedded clock, and lower power requirements. Figure 1 shows the CAN data stream, embedding the clock, inter alia, CAN message identifiers also include the frame start, address, data length of the code, data, CRC and frame end identifier. Analysis and serial data is usually triggered by much more difficult than the parallel data.Therefore, the oscilloscope manufacturer currently offers a varietyof serial data triggering capabilities, search features and protocols observation procedures to help you find the attention the incident, andits decoding and measurement. For example, AgilentInfiniium90000A Seriesoscilloscope with the serial data analysis software package that supports a large number of agreements, including CAN, LIN, I2C, SPI, Flexray, SAS, SATA, XAUI, Fiber Channel, DVI / HDMI, Infiniband, and PCI-express (1.1 and 2.0).With the continuous emergence of such an agreement, and a new generation of protocols to enter the market, oscilloscope suppliers, must keep up with the pace of development of new technologies, allowing users to effectively use these agreements work.Mixed-signal oscilloscope (MSO) was 10 years ago by Hewlett-Packard / Agilent Technologies Inc. introduced the first time. It is a comprehensive test instrument, with the availability of the oscilloscope and logic analyzer measurement capabilities as well as some serial protocol analysis capabilities. In the MSO's display, you can view a variety of time-ordered analogue and digital waveforms. Although the MSO failed to provide logic analyzer can provide all the channels (MSO usually 2 to 4 analog inputs and about 16 digital inputs), but its use can make up for it. Logic analyzer is too complex and difficult to use, while the oscilloscope is relatively easy. This is precisely the advantage of MSO - set the strengths of a variety of test equipment, and in between them to find the perfect balance.MSO is a popular for the current technology embedded mixed-signal system created. For example, automotive electronic systems tend to have a digitally-controlled analog motor controller and sensors. In the past, people often choose a traditional oscilloscope to analyze such systems, but often not enough to trigger the oscilloscope capability and input channels. Thus, one must also use the logic analyzer, resulting in more complex setup and operation.MSO completely solve this problem, and have been verified, an analysis of embedded mixed-signal system, the best equipment.Trend 3: The powerful, portable oscilloscopes / Custom Universal oscilloscope360毕业设计网 In the past, high-performance oscilloscope size are huge, portable oscilloscope performance, with the lower, and the user can only be one of the two. Modern high-speed serial data design and the urgent need for many people a portable high-performance oscilloscope. AgilentInfiniiVision7000 Series oscilloscope should be born, even though its volume is small (6.5 inches deep and weighing 13 pounds), but with a MegaZoomIII deep memory, 100000 waveforms / sec update rate, hardware-accelerated serial triggering and decoding capabilities. This allows you to have a highly portable, high-performance oscilloscope.示波器示波器是一种用途十分广泛的电子测量仪器。
Waves效果器全套中英文说对照表02.API-2500 压缩处理03.API-550A 均衡处理04.API-550B 均衡处理05.API-560 均衡处理06.AudioTrack 多重效果07.C1 Comp 压缩处理08.C1 Comp-Gate 压缩+门限09.C1 Comp-Sc 侧链压缩10.C1 Gate 门限处理11.C4 多段动态12.DeEsser 齿音消除13.Doppler 声场调整14.Doubler 合唱效果15.Enigma 迷幻效果16.IDR 抖动处理17.L1-Ultramaximizer 母带处理19.L2 母带处理20.L3-MultiMaximizer 母带处理22.LinEq Broadband 均衡处理23.LinMB 多段动态24.MaxxBass 泛音处理25.MetaFlanger 镶边效果26.MondoMod 调制效果27.Morphoder 声码效果28.PAZ Analyzer 综合分析仪29.PAZ Frequency 频谱仪30.PAZ Meters 电平表31.PAZ Position 声场仪32.Qx-Paragraphic EQ 均衡处理33.RBass 泛音处理34.RChannel 多重效果35.RComp 压缩处理36.RDeEsser 齿音消除37.REQ bands 均衡处理38.RVerb 卷积混响39.RVox 压缩处理40.S1-Imager 立体声扩展41.S1-Shuffler 立体声扩展42.SoundShifter G Offline 变速变调43.SoundShifter P 变调处理44.SoundShifter P Offline 变速变调45.SSLChannel 通道条46.SSLComp 压缩处理47.SSLEQ 均衡处理48.SuperTap 延迟处理49.TransX 瞬间电平控制50.TrueVerb 模拟混响51.UltraPitch 自动和声52.X-Click 去咔哒声53.X-Crackle 去噼啪声54.X-Hum 滤波降噪55.X-Noise 采样降噪56.Z-Noise 采样降噪57.DeBreath 去呼吸声58.Tune 音高修正。
July 24,200813:6WSPC/244-AADA 00004Advances in Adaptive Data Analysis 1Vol.1,No.1(2008)1–41c World Scientific Publishing Company 3ENSEMBLE EMPIRICAL MODE DECOMPOSITION:A NOISE ASSISTED DATA ANALYSIS METHOD 5ZHAOHUA WU ∗and NORDEN E.HUANG †∗Center for Ocean–Land–Atmosphere Studies 74041Powder Mill Road,Suite 302Calverton,MD 20705,USA 9†Research Center for Adaptive Data Analysis National Central University 11300Jhongda Road,Chungli,Taiwan 32001A new Ensemble Empirical Mode Decomposition (EEMD)is presented.This new 13approach consists of sifting an ensemble of white noise-added signal and treats the mean as the final true result.Finite,not infinitesimal,amplitude white noise is necessary to 15force the ensemble to exhaust all possible solutions in the sifting process,thus mak-ing the different scale signals to collate in the proper intrinsic mode functions (IMF)17dictated by the dyadic filter banks.As the EMD is a time–space analysis method,the white noise is averaged out with sufficient number of trials;the only persistent part 19that survives the averaging process is the signal,which is then treated as the true and more physical meaningful answer.The effect of the added white noise is to provide a 21uniform reference frame in the time–frequency space;therefore,the added noise collates the portion of the signal of comparable scale in one IMF.With this ensemble mean,one 23can separate scales naturally without any a priori subjective criterion selection as in the intermittence test for the original EMD algorithm.This new approach utilizes the 25full advantage of the statistical characteristics of white noise to perturb the signal in its true solution neighborhood,and to cancel itself out after serving its purpose;therefore,it 27represents a substantial improvement over the original EMD and is a truly noise-assisted data analysis (NADA)method.29Keywords :1.Introduction 31Empirical Mode Decomposition (EMD)has been proposed recently 1,2as an adap-tive time–frequency data analysis method.It has proven to be quite versatile in 33a broad range of applications for extracting signals from data generated in noisy nonlinear and nonstationary processes (see,for example,Refs.3and 4).As useful 35as EMD proved to be,it still leaves some annoying difficulties unresolved.One of the major drawbacks of the original EMD is the frequent appearance 37of mode mixing,which is defined as a single Intrinsic Mode Function (IMF)either consisting of signals of widely disparate scales,or a signal of a similar scale residing39in different IMF components.Mode mixing is a consequence of signal intermittency.1July24,200813:6WSPC/244-AADA000042Z.Wu&N.E.HuangAs discussed by Huang et al.,1,2the intermittence could not only cause serious 1aliasing in the time–frequency distribution,but also make the physical meaningof individual IMF unclear.To alleviate this drawback,Huang et al.2proposed the 3intermittence test,which can indeed ameliorate some of the difficulties.However,the approach itself has its own problems:First,the intermittence test is based on 5a subjectively selected scale.With this subjective intervention,the EMD ceases tobe totally adaptive.Secondly,the subjective selection of scales works if there are 7clearly separable and definable timescales in the data.In case the scales are notclearly separable but mixed over a range continuously,as in the case of the majority 9of natural or man-made signals,the intermittence test algorithm with subjectivelydefined timescales often does not work very well.11To overcome the scale separation problem without introducing a subjective intermittence test,a new noise-assisted data analysis(NADA)method is proposed, 13the Ensemble EMD(EEMD),which defines the true IMF components as the meanof an ensemble of trials,each consisting of the signal plus a white noise offinite 15amplitude.With this ensemble approach,we can clearly separate the scale nat-urally without any a priori subjective criterion selection.This new approach is 17based on the insight gleaned from recent studies of the statistical properties ofwhite noise,5,6which showed that the EMD is effectively an adaptive dyadicfilter 19bank a when applied to white noise.More critically,the new approach is inspired bythe noise-added analyses initiated by Flandrin et al.7and Gledhill.8Their results 21demonstrated that noise could help data analysis in the EMD.The principle of the EEMD is simple:the added white noise would populate 23the whole time–frequency space uniformly with the constituting components ofdifferent scales.When signal is added to this uniformly distributed white back-25ground,the bits of signal of different scales are automatically projected onto properscales of reference established by the white noise in the background.Of course, 27each individual trial may produce very noisy results,for each of the noise-addeddecompositions consists of the signal and the added white noise.Since the noise in 29each trial is different in separate trials,it is canceled out in the ensemble mean ofenough trials.The ensemble mean is treated as the true answer,for,in the end, 31the only persistent part is the signal as more and more trials are added in theensemble.33The critical concept advanced here is based on the following observations:1.A collection of white noise cancels each other out in a time-space ensemble mean;35therefore,only the signal can survive and persist in thefinal noise-added signalensemble mean.37a A dyadicfilter bank is a collection of band-passfilters that have a constant band-pass shape(e.g.a Gaussian distribution)but with neighboringfilters covering half or double of the frequencyrange of any singlefilter in the bank.The frequency ranges of thefilters can be overlapped.Forexample,a simple dyadicfilter bank can includefilters covering frequency windows such as50to120Hz,100to240Hz,200to480Hz,etc.July24,200813:6WSPC/244-AADA00004Ensemble Empirical Mode Decomposition32.Finite,not infinitesimal,amplitude white noise is necessary to force the ensemble1to exhaust all possible solutions;thefinite magnitude noise makes the differentscale signals reside in the corresponding IMF,dictated by the dyadicfilter banks, 3and render the resulting ensemble mean more meaningful.3.The true and physically meaningful answer to the EMD is not the one without5noise;it is designated to be the ensemble mean of a large number of trialsconsisting of the noise-added signal.7This EEMD proposed here has utilized all these important statistical character-istics of noise.We will show that the EEMD utilizes the scale separation principle 9of the EMD,and enables the EMD method to be a truly dyadicfilter bank forany data.By addingfinite noise,the EEMD eliminates mode mixing in all cases 11automatically.Therefore,the EEMD represents a major improvement of the EMDmethod.13In the following sections,a systematic exploration of the relation between noise and signal in data will be presented.Studies of Flandrin et al.5and Wu and Huang6 15have revealed that the EMD serves as a dyadicfilter for various types of noise.Thisimplies that a signal of a similar scale in a noisy data set could possibly be contained 17in one IMF component.It will be shown that adding noise withfinite rather thaninfinitesimal amplitude to data indeed creates such a noisy data set;therefore, 19the added noise,havingfilled all the scale space uniformly,can help to eliminatethe annoying mode mixing problemfirst noticed by Huang et al.2Based on these 21results,we will propose formally the concepts of NADA and noise-assisted signalextraction(NASE),and will develop a method called the EEMD,which is based 23on the original EMD method,to make NADA and NASE possible.The paper is arranged as follows.Section2will summarize previous attempts of 25using noise as a tool in data analysis.Section3will introduce the EEMD method,illustrate more details of the drawbacks associated with mode mixing,present con-27cepts of NADA and of NASE,and introduce the EEMD in detail.Section4willdisplay the usefulness and capability of the EEMD through examples.Section5 29will further discuss the related issues to the EEMD,its drawbacks,and their corre-sponding solutions.A summary and discussion will be presented in thefinal section 31of the main text.Two appendices will discuss some related issues of EMD algorithmand a Matlab EMD/EEMD software for research community to use.332.A Brief Survey of Noise Assisted Data AnalysisThe word“noise”can be traced etymologically back to its Latin root of“nausea,”35meaning“seasickness.”Only in Middle English and Old French does it start to gainthe meaning of“noisy strife and quarrel,”indicating something not at all desirable.37Today,the definition of noise varies in different circumstances.In science and engi-neering,noise is defined as disturbance,especially a random and persistent kind 39that obscures or reduces the clarity of a signal.In natural phenomena,noise couldJuly24,200813:6WSPC/244-AADA000044Z.Wu&N.E.Huangbe induced by the process itself,such as local and intermittent instabilities,irresolv-1able subgrid phenomena,or some concurrent processes in the environment in whichthe investigations are conducted.It could also be generated by the sensors and 3recording systems when observations are made.When efforts are made to under-stand data,important differences must be considered between the clean signals that 5are the direct results of the underlying fundamental physical processes of our inter-est(“the truth”)and the noise induced by various other processes that somehow 7must be removed.In general,all data are amalgamations of signal and noise,i.e.x(t)=s(t)+n(t),(1) 9in which x(t)is the recorded data,and s(t)and n(t)are the true signal andnoise,respectively.Because noise is ubiquitous and represents a highly undesirable 11and dreaded part of any data,many data analysis methods were designed specifi-cally to remove the noise and extract the true signals in data,although often not 13successful.Since separating the signal and the noise in data is necessary,three important 15issues should be addressed:(1)The dependence of the results on the analysis meth-ods used and assumptions made on the data.(For example,a linear regression of 17data implicitly assumes the underlying physics of the data to be linear,while aspectrum analysis of data implies the process is stationary.)(2)The noise level to 19be tolerated in the extracted“signals,”for no analysis method is perfect,and inalmost all cases the extracted“signals”still contain some noise.(3)The portion 21of real signal obliterated or deformed through the analysis processing as part ofthe noise.(For example,Fourierfiltering can remove harmonics through low-pass 23filtering and thus deform the waveform of the fundamentals.)All these problems cause misinterpretation of data,and the latter two issues are 25specifically related to the existence and removal of noise.As noise is ubiquitous,steps must be taken to insure that any meaningful result from the analysis should 27not be contaminated by noise.To avoid possible illusion,the null hypothesis testagainst noise is often used with the known noise characteristics associated with the 29analysis method.6,9,7Although most data analysis techniques are designed specifi-cally to remove noise,there are,however,cases when noise is added in order to help 31data analysis,to assist the detection of weak signals,and to delineate the under-lying processes.The intention here is to provide a brief survey of the beneficial 33utilization of noise in data analysis.The earliest known utilization of noise in aiding data analysis was due to Press 35and Tukey10known as pre-whitening,where white noise was added toflatten thenarrow spectral peaks in order to get a better spectral estimation.Since then, 37pre-whitening has become a very common technique in data analysis.For exam-ple,Fuenzalida and Rosenbluth11added noise to process climate data;Link and 39Buckley,12and Zala et al.13used noise to improve acoustic signal;Strickland andIl Hahn14used wavelet and added noise to detect objects in general;and Trucco15 41used noise to help design specialfilters for detecting embedded objects on the oceanJuly24,200813:6WSPC/244-AADA00004Ensemble Empirical Mode Decomposition5floor experimentally.Some general problems associated with this approach can be 1found in the works by Priestley,16Kao et al.,17Politis,18and Douglas et al.19 Another category of popular use of noise in data analysis is more related to the 3analysis method than to help extracting the signal from the data.Adding noiseto data helps to understand the sensitivity of an analysis method to noise and 5the robustness of the results obtained.This approach is used widely;for example,Cichocki and Amari20added noise to various data to test the robustness of the 7independent component analysis(ICA)algorithm,and De Lathauwer et al.21usednoise to identify error in ICA.9Adding noise to the input to specifically designed nonlinear detectors could also be beneficial to detecting weak periodic or quasi-periodic signals based on a physical 11process called stochastic resonance.The study of stochastic resonance was pioneeredby Benzi and his colleagues in the early1980s.The details of the development of 13the theory of stochastic resonance and its applications can be found in a lengthyreview paper by Gammaitoni et al.22It should be noted here that most of the 15past applications(including those mentioned earlier)have not used the cancellationeffects associated with an ensemble of noise-added cases to improve their results.17Specific to analysis using EMD,Huang et al.23added infinitesimal magnitude noise to earthquake data in an attempt to prevent the low frequency mode from 19expanding into the quiescent region.But they failed to realize fully the implicationsof the added noise in the EMD method.The true advances related to the EMD 21method had to wait until the two pioneering works by Gledhill8and Flandrin et al.7 Flandrin et al.7used added noise to overcome one of the difficulties of the 23original EMD method.As the EMD is solely based on the existence of extrema(either in amplitude or in curvature),the method ceases to work if the data lacks 25the necessary extrema.An extreme example is in the decomposition of a Diracpulse(delta function),where there is only one extrema in the whole data set.To 27overcome the difficulty,Flandrin et al.7suggested adding noise with infinitesimalamplitude to the Dirac pulse so as to make the EMD algorithm operable.Since 29the decomposition results are sensitive to the added noise,Flandrin et al.7ran anensemble of5000decompositions,with different versions of noise,all of infinitesimal 31amplitude.Though they used the mean as thefinal decomposition of the Diracpulse,they defined the true answer as33E{d[n]+εr k[n]},(2)d[n]=lime→0+in which,[n]represents n th data point,d[n]is the Dirac function,r k[n]is a random 35number,εis the infinitesimal parameter,and E{}is the expected value.Flandrin’snovel use of the added noise has made the EMD algorithm operable for a data set 37that could not be previously analyzed.Another novel use of noise in data analysis is by Gledhill,8who used noise to 39test the robustness of the EMD algorithm.Although an ensemble of noise was used,he never used the cancellation principle to define the ensemble mean as the true 41answer.Based on his discovery(that noise could cause the EMD to produce slightlyJuly24,200813:6WSPC/244-AADA000046Z.Wu&N.E.Huangdifferent outcomes),he assumed that the result from the clean data without noise 1was the true answer and thus designated it as the reference.He then defined thediscrepancy,∆,as3∆=mj=1t(cr j(t)−cn j(t))21/2,(3)where cr j and cn j are the j th component of the IMF without and with noise added, 5and m is the total number of IMFs generated from the data.In his extensive study of the detailed distribution of the noise-caused“discrepancy,”he concluded that 7the EMD algorithm is reasonably stable for small perturbations.This conclusion is in slight conflict with his observations that the perturbed answer with infinitesimal 9noise showed a bimodal distribution of the discrepancy.Gledhill had also pushed the noise-added analysis in another direction:He had 11proposed to use an ensemble mean of noise-added analysis to form a“Composite Hilbert spectrum.”As the spectrum is non-negative,the added noise could not 13cancel out.He then proposed to keep a noise-only spectrum and subtract it from the full noise-added spectrum at the end.This non-cancellation of noise in the 15spectrum,however,forced Gledhill8to limit the noise used to be of small magnitude, so that he could be sure that there would not be too much interaction between the 17noise-added and the original clean signal,and that the contribution of the noise to thefinal energy density in the spectrum would be negligible.19Although noise of infinitesimal amplitude used by Gledhill8has improved the confidence limit of thefinal spectrum,Gledhill explored neither fully the cancella-21tion property of the noise nor the power offinite perturbation to explore all possible solutions.Furthermore,it is well known that whenever there is intermittence,the 23signal without noise can produce IMFs with mode mixing.There is no justification to assume that the result without added noise is the truth or the reference sig-25nal.These reservations notwithstanding,all these studies by Flandrin et al.7and Gledhill8had still greatly advanced the understanding of the effects of noise in the 27EMD method,though the crucial effects of noise had yet to be clearly articulated and fully explored.29In the following,the new noise-added EMD approach will be explained,in which the cancellation principle will be fully utilized,even withfinite amplitude noise.Also 31emphasized is thefinding that the true solution of the EMD method should be the ensemble mean rather than the clean data.This full presentation of the new method 33will be the subject of the next section.3.Ensemble Empirical Mode Decomposition353.1.The empirical mode decompositionThis section starts with a brief review of the original EMD method.The detailed 37method can be found in the works of Huang et al.1and Huang et al.2Different to almost all previous methods of data analysis,the EMD method is adaptive,with 39July24,200813:6WSPC/244-AADA00004Ensemble Empirical Mode Decomposition7 the basis of the decomposition based on and derived from the data.In the EMD 1approach,the data X(t)is decomposed in terms of IMFs,c j,i.e.x(t)=nj=1c j+r n,(4)3where r n is the residue of data x(t),after n number of IMFs are extracted.IMFs are simple oscillatory functions with varying amplitude and frequency,and hence 5have the following properties:1.Throughout the whole length of a single IMF,the number of extrema and the 7number of zero-crossings must either be equal or differ at most by one(althoughthese numbers could differ significantly for the original data set);92.At any data location,the mean value of the envelope defined by the local maximaand the envelope defined by the local minima is zero.11In practice,the EMD is implemented through a sifting process that uses only local extrema.From any data r j−1,say,the procedure is as follows:(1)identify all 13the local extrema(the combination of both maxima and minima)and connect all these local maxima(minima)with a cubic spline as the upper(lower)envelope; 15(2)obtain thefirst component h by taking the difference between the data and thelocal mean of the two envelopes;and(3)Treat h as the data and repeat steps1and 172as many times as is required until the envelopes are symmetric with respect to zero mean under certain criteria.Thefinal h is designated as c j.A complete sifting 19process stops when the residue,r n,becomes a monotonic function from which no more IMFs can be extracted.21Based on this simple description of EMD,Flandrin et al.5and Wu and Huang6 have shown that,if the data consisted of white noise which has scales populated 23uniformly through the whole timescale or time–frequency space,the EMD behaves as a dyadicfilter bank:the Fourier spectra of various IMFs collapse to a single 25shape along the axis of logarithm of period or frequency.Then the total number of IMFs of a data set is close to log2N with N the number of total data points. 27When the data is not pure noise,some scales could be missing;therefore,the total number of the IMFs might be fewer than log2N.Additionally,the intermittency 29of signals in certain scale would also cause mode mixing.3.2.Mode mixing problem31“Mode mixing”is defined as any IMF consisting of oscillations of dramatically dis-parate scales,mostly caused by intermittency of the driving mechanisms.When 33mode mixing occurs,an IMF can cease to have physical meaning by itself,suggest-ing falsely that there may be different physical processes represented in a mode. 35Even though thefinal time–frequency projection could rectify the mixed mode to some degree,the alias at each transition from one scale to another would irrecov-37erably damage the clean separation of scales.Such a drawback wasfirst illustratedJuly24,200813:6WSPC/244-AADA000048Z.Wu&N.E.Huangby Huang et al.2in which the modeled data was a mixture of intermittent high-1frequency oscillations riding on a continuous low-frequency sinusoidal signal.Analmost identical example used by Huang et al.2is presented here in detail as an 3illustration.The data and its sifting process are illustrated in Fig.1.The data has its funda-5mental part as a low-frequency sinusoidal wave with unit amplitude.At the threemiddle crests of the low-frequency wave,high-frequency intermittent oscillations 7with an amplitude of0.1are riding on the fundamental,as panel(a)of Fig.1shows.The sifting process starts with identifying the maxima(minima)in the 9data.In this case,15local maxima are identified,with thefirst and the last comingfrom the fundamental,and the other13caused mainly by intermittent oscillations 11(panel(b)).As a result,the upper envelope resembles neither the upper envelope ofthe fundamental(which is aflat line at one)nor the upper one of the intermittent 13oscillations(which is supposed to be the fundamental outside intermittent areas).Rather,the envelope is a mixture of the envelopes of the fundamental and of the 15(a)(b)(c)(d)Fig.1.The veryfirst step of the sifting process.Panel(a)is the input;panel(b)identifies localmaxima(gray dots);panel(c)plots the upper envelope(upper gray dashed line)and low envelope(lower gray dashed line)and their mean(bold gray line);and panel(d)is the difference betweenthe input and the mean of the envelopes.July24,200813:6WSPC/244-AADA00004Ensemble Empirical Mode Decomposition9Fig.2.The intrinsic mode functions of the input displayed in Fig.1(a).intermittent signals that lead to a severely distorted envelope mean(the thick grey 1line in panel(c)).Consequently,the initial guess of thefirst IMF(panel(d))is themixture of both the low frequency fundamental and the high-frequency intermittent 3waves,as shown in Fig.2.An annoying implication of such scale mixing is related to unstableness and lack 5of the uniqueness of decomposition using the EMD.With stoppage criterion givenand end-point approach prescribed in the EMD,the application of the EMD to 7any real data results in a unique set of IMFs,just as when the data is processedby other data decomposition methods.This uniqueness is here referred to as“the 9mathematical uniqueness,”and satisfaction to the mathematical uniqueness is theminimal requirement for any decomposition method.The issue that is emphasized 11here is what we refer to as“the physical uniqueness.”Since real data almost alwayscontains a certain amount of random noise or intermittences that are not known 13to us,an important issue,therefore,is whether the decomposition is sensitive tonoise.If the decomposition is insensitive to added noise of small butfinite ampli-15tude and bears little quantitative and no qualitative change,the decomposition isgenerally considered stable and satisfies the physical uniqueness;and otherwise, 17the decomposition is unstable and does not satisfy the physical uniqueness.Theresult from decomposition that does not satisfy the physical uniqueness may not be 19reliable and may not be suitable for physical interpretation.For many traditionaldata decomposition methods with prescribed base functions,the uniqueness of the 21July24,200813:6WSPC/244-AADA0000410Z.Wu&N.E.Huangsecond kind is automatically satisfied.Unfortunately,the EMD in general does not 1satisfy this requirement due to the fact that decomposition is solely based on thedistribution of extrema.3To alleviate this drawback,Huang et al.2proposed an intermittence test that subjectively extracts the oscillations with periods significantly smaller than a pre-5selected value during the sifting process.The method works quite well for thisexample.However,for complicated data with scales variable and continuously dis-7tributed,no single criterion of intermittence test can be selected.Furthermore,themost troublesome aspect of this subjectively pre-selected criterion is that it lacks 9physical justifications and renders the EMD nonadaptive.Additionally,mode mix-ing is also the main reason that renders the EMD algorithm unstable:Any small 11perturbation may result in a new set of IMFs as reported by Gledhill.8Obviously,the intermittence prevents EMD from extracting any signal with similar scales.13To solve these problems,the EEMD is proposed,which will be described in thefollowing sections.153.3.Ensemble empirical mode decompositionAs given in Eq.(1),all data are amalgamations of signal and noise.To improve the 17accuracy of measurements,the ensemble mean is a powerful approach,where dataare collected by separate observations,each of which contains different noise.To 19generalize this ensemble idea,noise is introduced to the single data set,x(t),as ifseparate observations were indeed being made as an analog to a physical experiment 21that could be repeated many times.The added white noise is treated as the possiblerandom noise that would be encountered in the measurement process.Under such 23conditions,the i th“artificial”observation will bex i(t)=x(t)+w i(t).(5) 25In the case of only one observation,one of the multiple-observation ensembles is mimicked by adding not arbitrary but different copies of white noise,w i(t),to 27that single observation as given in Eq.(5).Although adding noise may result insmaller signal-to-noise ratio,the added white noise will provide a uniform reference 29scale distribution to facilitate EMD;therefore,the low signal–noise ratio does notaffect the decomposition method but actually enhances it to avoid the mode mixing.31Based on this argument,an additional step is taken by arguing that adding whitenoise may help to extract the true signals in the data,a method that is termed 33EEMD,a truly NADA method.Before looking at the details of the new EEMD,a review of a few properties of 35the original EMD is presented:(1)the EMD is an adaptive data analysis method that is based on local charac-37teristics of the data,and hence,it catches nonlinear,nonstationary oscillationsmore effectively;39。
a r X i v :c o n d -m a t /0401631v 1 [c o n d -m a t .m e s -h a l l ] 30 J a n 2004CondMat 2004Surface Acoustic Waves probe of the p -type Si/SiGe heterostructuresG.O.Andrianov,I.L.Drichko,A.M.Diakonov,and I.Yu.SmirnovA.F.Ioffe Physicotechnical Institute of RAS,194021St.Petersburg,RussiaO.A.Mironov,M.Myronov,T.E.Whall,and D.R.LeadleyDepartment of Physics,University of Warwick,Coventry CV47AL,UK(Dated:February 2,2008)The surface acoustic wave (SAWs)attenuation coefficient Γand the velocity change ∆V /V were measured for p -type Si/SiGe heterostructures in the temperature range 0.7-1.6K as a function of external magnetic field H up to 7T and in the frequency range 30-300MHz in the hole Si/SiGe heterostructures.Oscillations of Γ(H)and ∆V /V (H)in a magnetic field were observed.Both real σ1(H)and imaginary σ2(H)components of the high-frequency conductivity have been determined.Analysis of the σ1to σ2ratio allows the carrier localization to be followed as a function of tem-perature and magnetic field.At T=0.7K the variation of Γ,∆V /V and σ1with SAW intensity have been studied and could be attributed to 2DHG heating by the SAW electric field.The energy relaxation time is found to be dominated by scattering at the deformation potential of the acoustic phonons with weak screening.PACS numbers:73.63.Kv,72.20.Ee,85.50.-nI.INTRODUCTIONFor the first time an acoustic method has been ap-plied in a study of p -type Si/SiGe heterostructures.Since Ge and Si are not piezoelectrics the only way to mea-sure acoustoelectric effects in these systems is a hy-brid method:a SAW propagates along the surface of a piezoelectric LiNbO 3while the Si/SiGe sample is be-ing slightly pressed onto LiNbO 3surface by means of a spring.In this case a strain from the SAW is not transmitted to the sample and it is only the electric field accompanying the SAW that penetrates into the sam-ple and creates currents that,in turn,produce a feed-back to the SAW.As a result,both SAW attenuation Γand velocity V appear to depend on the properties of the 2DHG.SAW-acoustics proves to be an effective probe of heterostructure parameters,especially as it is contactless and does not require the Hall-bar configura-tion of a sample.Moreover,simultaneous measurements of attenuation and velocity of SAW provide a unique possibility to determine the complex AC conductivity,σxx (ω)=σ1(ω)−iσ2(ω),as a function of magnetic field H and SAW frequency ω.Furthermore,the magnetic field dependence of σxx (ω)provides information on both the extended and localized states 1.II.EXPERIMENTAL RESULTSThe absorption Γand velocity shift ∆V/V of the SAW,that interacts with 2DHG in the SiGe channel,have been measured at temperatures T=0.7-1.6K in magnetic fields up to H=7T.DC-measurements of the resistivity compo-nents ρxx and ρxy have also been carried out in magnetic fields up to 11T in the temperature range 0.3-1.3K and have shown the integer quantum Hall effect.The samples were modulation doped Si/SiGe het-erostructures with 2DHG sheet density p =2×1011cm −2and mobility µ=10500cm 2/Vs 2.Fig.1illustrates the field dependences of Γand ∆V/V for the frequency 30MHz at T=0.7K as well as com-ponents of the magnetoresistance.One can see the ab-sorption coefficient and the velocity shift both undergo SdH-type oscillations.FIG.1:Dependences of Γ(H)and ∆V /V (H),f =30MHz,T=0.7K;ρxx and ρxy vs H,T=0.7K.High frequency conductivity σACxx =σ1−iσ2is extracted from simultaneous measurements of Γand ∆V/V ,using eqs.1-5of 1.It turns out,that at T=0.7K and filling factor ν=2(H=4.3T)σ1≃σ2(fig.2).At the same time σ1≫σdcxx .These facts suggest that only some of holes in the 2D-channel are localized,and σACxx is determined by both localized and delocalized holes.For total localization oneneeds σ1≪σ2,σDCxx =03.At ν=3(H=2.9T)localizationeffects are negligible:σ1≃σdcxx >σ2.At T=0.7K we have measured the dependences of Γ(H),∆V/V (H)and σ1(H)on the SAW intensity at2FIG.2:Magneticfield dependences ofσ1(solid),σ2(dashed)(dotted);all at T=0.7K.at f=30MHz andσDCxx30MHz.Fig.3a showsσ1versus P(the RF-sourcepower)for magneticfields of H=2.9T and4.3T.Fig.3billustrates the temperature dependence ofσ1measured inthe linear regime.One can see from these plots thatσ1increases with increasing temperature and SAW power.For delocalized holes in this magneticfield,the ob-served nonlinear effects(Fig.3a)are probably associatedwith carrier heating.One may describe2DHG heating4by means of a carrier temperature T c,greater than thelattice temperature T,provided that the following con-dition is met:τ0<<τcc<<τε.(1)Hereτ0,τcc andτεare the momentum relaxationtime,the carrier-carrier interaction time and the en-ergy relaxation time,respectively.Calculations giveτ0=1.4×10−12s;τcc=6.4×10−11s5;τεwill be dis-cussed below.The carrier temperature T c is determined using SdHthermometry by comparing the dependencesσ1(T)andσ1(P).To characterize heating process one needs to extractthe absolute energy losses as a result of the SAW inter-action with the carriers¯Q=σxx E2=4ΓW,where W isthe input SAW power scaled to the width of the soundtrack,E is the intensity of the SAW electricfield6:|E|2=K232π1+(4πσAC xx2m2ζ(5)D2ac k5B3 can determine the value of the deformation potential asD ac=5.3±0.3eV.The value of D ac calculated from DCmeasurements of phonon-drag thermopower was reportedto be5.5±0.5eV for the same2DHG Si/SiGe sample8.AcknowledgmentsThe work was supported by RFFI,NATO-CLG979355,INTAS-01-084,Prg.MinNauki”Spintronika”.1I.L.Drichko, A.M.Diakonov,I.Yu.Smirnov, Y.M.Galperin,and A.I.Toropov,Phys.Rev.B62, 7470(2000).2T.E.Whall,N.L.Mattey, A.D.Plews,P.J.Phillips, O.A.Mironov,R.J.Nicholas and M.J.Kearney,Appl.Phys. Lett.64,357(1994).3A.L.Efros,ZETF89,1834(1985)[JETP89,1057(1985)]. 4G.Ansaripour,G.Braithwaite,M.Myronov,O.A.Mironov, E.H.C.Parker and T.E.Whall,Appl.Phys.Lett.76,1140 (2000).5Yu.F.Komnik,V.V.Andrievskii,I.B.Berkutov,S.S.Kry-achko,M.Myronov and T.E.Whall,Low Temp.Phys.26609(2000)[Fiz.Nizk.Temp.26,829(2000)].6I.L.Drichko, A.M.Diakonov,V.D.Kagan, A.M.Kreshchuk,T.A.Polyanskaya,I.G.Savel’ev, I.Yu.Smirnov and A.V.Suslov.FTP31,1357(1997) [Semiconductors31,1170(1997)].7V.Karpus,FTP20,12(1986)[Sov.Phys.Semicond.20,6 (1986)].8S.Agan,O.A.Mironov,M.Tsaousidou,T.E.Whall, E.H.C.Parker,P.N.Butcher,Microelectronic Engineering 51-52,527(2000).。
常用音乐术语全集1、套曲 Cycle 一种由多乐章组合而成的大型器乐曲或声乐器2、组曲 Suite由几个具有相对独立性的器乐曲组成的乐曲3、奏鸣曲Sonata指类似组曲的器乐合奏套曲.自海顿.莫扎特以后,其指由3-4个乐章组成的器乐独奏套曲（钢琴奏鸣曲）或独奏乐器与钢琴合奏的器乐曲（小提琴奏鸣曲）4、交响曲 symphony大型管弦乐套曲，通常含四个乐章.其乐章结构与独奏的奏鸣曲相同5、协奏曲concerto由一件或多件独奏乐器与管弦乐团相互竞奏,并显示其个性及技巧的大型器乐套曲.分独奏协奏曲、大协奏曲、小协奏曲等6、交响诗 symphonic poem单乐章的标题****响音乐7、音诗 poeme单乐章管弦乐曲，与交响诗相类似8、序曲 overture歌剧、清唱剧、舞剧、其他戏剧作品和声乐、器乐套曲的开始曲。
十九世纪又出现独立的音乐会序曲前奏曲prelude带有即兴曲的性质、有独立的乐思、常放在具有严谨结构的乐曲或套曲之前作为序引的中、小型器乐曲。
10、托卡塔 toccata 节奏紧凑、快速触键的富有自由即兴性的键盘乐曲cantabile 一如歌地 con spirito 一有精神地deciso 一坚定地doice 一柔和地dolente 一怨诉地energico—精力充沛地fantastico 一幻想地grave 一沉重地grazioso ―优雅地giocoso—嬉戏地leggiero一轻巧地largamente一宽广地maestoso一庄严地marcato 一强调mesto 一忧伤地nobilmente —高雅地pathetic 一悲怆地passionate 一热情洋溢地pastoral 一田园地risoluto 一果断地Rubato 一节奏自由sempl ice 一朴素地sempre -继续地 sentimento 一多愁善感地sostenuto -持续地 vivace 一活泼地vivo 一活跃地scherzando 一幽默地spirito 一精神饱满地tranquillo一安静地触键术语glissando 滑奏legato连音legato assai 很连贯legatissimo 最连音non legato非连音portato次断音staccatostaccatissimo▲断音sempre slacc一直用断音tenuto保持Aart achievement [9? tfi:vmsnt]艺术成就1.artistic [a / tistik] appeal [a'pi: 1]艺术感染力artistic image 艺术形象2.amateur ['kmat。
黄金版管弦乐CELLOS 大提琴VIOLAS 小提琴VIOLINS 中提琴CLARINTS 单簧管，黑管FLUTES 长笛OBOES 双簧管TROMBINES 长号TRUMPETS 军号FRENCH HORNS 法国号DOUBLE BASSES 低音提琴ALTO FLUTE 高音长笛BASS CLARINET 低音黑管BASSOON 低音管, 巴颂管CELLO 大提琴CLARINET 竖笛CONCER FLUTE 音乐会长笛CONTRA BASSOON 低音单簧管ENGLISH HORN 英国管FRENCH HORN 法国号HARP 竖琴OBOE 双簧管ORCHESTRAL PERC 交响乐中的打击乐PICCOLO FLUTE 短笛TROMBONE 长号TRUMPET 小号TUBA 大号VOLIN 小提琴表现术语EWQLSO技巧与表情术语缩写：QUICK-快速的UP DN-提琴上、下分弓SLOW-慢的SHORT（SHRT）-短促的EXP-有表现力的STAC-管乐断奏LEG-提琴的连弓RIPS-圆号撕裂奏法SORD-柔美 SLIDE-圆号滑音奏法ACCENT-重音 SHAKE-圆号颤抖奏法LYR-抒情的LAY-圆号平缓奏法MARC-断弓SMOOTH-圆号平滑奏法MART-短促而有力的断弓（顿弓） ADVENTURE-圆号冒险奏法（音头裂开，余音平缓）NON-VIB-不是很多的颤音MELLOW-柔美、温暖的奏法TREM-提琴弓震音FLUTTER-长号烦躁、沙哑的奏法TRILL-指震音（分半音和全音）FALL-半音下滑奏法PIZZ-提琴拨奏GRACE-两个短促的半音奏法SPIC-提琴跳弓HARD-生硬的奏法SUS-长音、持续音SOFT-柔软的奏法CLSTRA & AIR-小提琴上的怪异音 GLISS-双簧管半音上行奏法PORT-次重音SLR（SLUR）-小号上怪异的奏法FORTE-极强音 PIANO-微弱SFZ-强弱强 CREC-强F就是Front ——前置F,乐器前.C,舞台前.S,舞台观众席后方.C就是Center ——中间S就是Sourround ——环绕罗兰管弦乐音色表【中英文对照】PC Instrument Name 意义1 Violin Warm Section 丰满的小提琴组（带颤音）2 Slow Violin Section 舒缓的小提琴组3 Violin Section 适合快速演奏的小提琴组4 Violin Section /Vsw 适合快速演奏的小提琴组5 Violins spiccato 小提琴组跳弓6 Violins Pizzcato 小提琴组拨奏7 Troemolo Violins 小提琴组震音8 Viola Warm Section 丰满的中提琴组（带颤音）9 Slow Viola Section 舒缓的中提琴组10 Viola Section 适合快速演奏的中提琴组11 Viola Section /Vsw 适合快速演奏的中提琴组12 Violas spiccato 中提琴组跳弓13 Violas Pizzcato 中提琴组拨奏14 Troemolo Violas 中提琴组震音15 Cello Warm Section 丰满的大提琴组（带颤音）16 Cello Section 适合快速演奏的大提琴组17 Cello Section /Vsw 适合快速演奏的大提琴组18 Cellos spiccato 大提琴组跳弓19 Cellos Pizzcato 大提琴组拨奏20 Troemolo Cellos 大提琴组震音21 ContraBass Section 倍低音大提琴22 ContraBasses spicc 倍低音大提琴跳弓23 ContraBassesPizzcato 倍低音大提琴拨奏24 Troem ContraBasses 倍低音大提琴震音25 ContraBass&C.Basson 倍低音大提琴和巴松管的层状声音(fagott)26 ContraBasses/Cellos 倍低音大提琴和大提琴的分列声音。
Chapter 9 OscillationsWe are surrounded by oscillations─motions that repeat themselves. (1). There are swinging chandeliers, boats bobbing at anchor, and the surging pistons in the engines of cars. (2). There are oscillating guitar strings, drums, bells, diaphragms in telephones and speaker systems, and quartz crystals in wristwatches. (3). Less evident are the oscillations of the air molecules that transmit the sensation of sound, the oscillations of the atoms in a solid that convey the sensation of temperature, and the oscillations of the electrons in the antennas of radio and TV transmitters.Oscillations are not confined to material objects such as violin strings and electrons. Light, radio waves, x-rays, and gamma rays are also oscillatory phenomena. You will study such oscillations in later chapters and will be helped greatly there by analogy with the mechanical oscillations that are about to study here.Oscillations in the real world are usually damped; that is, the motion dies out gradually, transferring mechanical energy to thermal energy by the action of frictional force. Although we cannot totally eliminate such loss of mechanical energy, we can replenish the energy from some source.9.1 Simple Harmonic Motion1. The figure shows a sequenceof “snapshots” of a simpleoscillating system, a particlemoving repeatedly back andforth about the origin of the xaxis.2. Frequency: (1). One importantproperty of oscillatory motionis its frequency , or number ofoscillations that arecompleted each second . (2). The symbol for frequency is f, and (3) its SI unit is hertz (abbreviated Hz), where 1 hertz = 1 Hz = 1 oscillation per second = 1 s -1.3. Period: Related to the frequency is the period T of the motion, which is the time for one complete oscillation (or cycle). That is f T 1=.4. Any motion that repeats itself at regular intervals is called period motion or harmonic motion . We are interested here in motion that repeats itself in a particular way. It turns out that for such motion the displacement x of the particle from the origin is given as a function of time by )cos()(φω+=t x t x m , inwhich φωand x m ,, are constant. The motion is called simple harmonic motion (SHM), the term that means that the periodic motion is a sinusoidal of time .5. The quantity m x , a positive constant whose value depends onhow the motion was started, is called the amplitude of the motion; the subscript m stands for maximum displacement of the particle in either direction.6. The time-varying quantity )(φω+t is called the phase of the motion, and the constant φ is called the phase constant (or phase angle ). The value of φ depends on the displacement and velocity of the particle at t=0.7. It remains to interpret the constant ω. The displacement )(t x must return to its initial value after one period T of the motion. That is, )(t x must equal)(T t x + for all t. To simplify our analysis, we put 0=φ. So we then have)](cos[cos T t x t x m m +=ωω. The cosine function first repeats itself when its argument (the phase) has increased by π2 rad, so that we must haveπωπωω22)(=+=+T or t T t . It means f T ππω22==. The quantity ω is called the angular frequency of the motion; its SI unit is the radian per second.8. The velocity of SHM: (1). Take derivative of the displacement with time, we can find an expression for thevelocity of the particle moving with simple harmonic motion. That is, )2/cos()sin()()(πφωφωω++=+-==t v t x dtt dx t v m m . (2). The positive quantity m m x v ω= inabove equation is called thevelocity amplitude .9. The acceleration of SHM:Knowing the velocity for simpleharmonic motion, we can find anexpression for the acceleration ofthe oscillation particle by differentiating once more. Thus we have)cos()2/sin()()(πφωπφωω++=++-==t a t v dtt dv t a m m The positive quantity m m m x v a 2ωω== is called the accelerationamplitude . We can also to get )()(2t x t a ω-=, which is thehallmark of simple harmonic motion: the acceleration is proportional to the displacement but opposite in sign, and the two quantities are related by the square of the angular frequency .9.2 The Force Law For SHM1. Once we know how the acceleration of a particle varies with time, we can use Newton’s second law to learn what forc emust act on the particle to give it that acceleration. For simple harmonic motion, we have kxω. This result-a=-(2=)xmmaF-=force proportional to the displacement but opposite in sign-is something like Hook’s law for a spring, the spring constant here being 2ωmk=.2.We can in fact take above equation as an alternative definition of simple harmonic motion. It says: Simple harmonic motion is the motion executed by a particle of mass m subject to a force that is proportional to the displacement of the particle but opposite in sign.3.The block-spring system forms a linear simple harmonic oscillator(linearoscillator for short),where linear indicatesthat F is proportional to x rather than to some other power of x. (1). The angular frequencyωof the simple harmonic motion of the block is mω. (2). The period of the linear=k/oscillator is k=.2πmT/9.3Energy in Simple Harmonic Motion1.The potential energy of a linear oscillator depends on howmuch the spring is stretched or compressed, that is, on )(t x.We have )(cos 2121)(222φω+==t kx kx t U m . 2. The kinetic energy of the system depends on haw fast the block is moving, that is on)(t v . We have )(sin 21)(sin )(21)(sin 2121)(22222222φωφωφωω+=+=+==t kx t x m k m t x m mv t K m m m 3. The mechanical energy is2222221)(sin 21)(cos 21m m m kx t kx t kx K U E =+++=+=φωφω The mechanical energy of a linear oscillator is indeed a constant, independent of time.9.4 An Angular simple Harmonic Oscillator1. The figure shows an angular version of a simple harmonic oscillator; the element of springinessor elasticity is associated with thetwisting of a suspension wire ratherthan the extension and compressionof a spring as we previously had. Thedevice is called a torsion pendulum ,with torsion referring to the twisting.2. If we rotate the disk in the figure from its rest position and release it, it will oscillate about that position in angular simple harmonic motion. Rotating the disk through an angleθ in either direction introduce a restoring torque given byθκτ-=. Here κ (Greek kappa) is a constant, called the torsion constant , that depends on the length, diameter, and material of the suspension wire.3. From the parallelism between angular quantities and linear quantities (give a little more explanation), we have κπIT 2=for the period of the angular simple harmonic oscillator, or torsion pendulum.9.5 PendulumWe turn now to a class of simple harmonic oscillators in which the springiness is associated with the gravitational force rather than with the elastic properties of a twisted wire or a compressed or stretched spring.1. The Simple Pendulum(1). We consider a simplependulum, which consists ofa particle of mass m (calledthe bob of the pendulum)suspended from an un-stretchable, massless string of length L , as in the figure. The bob is free to swing back and forth in the plane of the page, to the left and right of a vertical linethrough the point at which the upper end of the string is fixed.(2). The forces acting the particle, shown in figure (b), are its weight and the tension in the string. The restoring force is the tangent component of the weight θsin mg , which is always acts opposite the displacement of the particle so as to bring the particle back toward its central location, the equilibrium (0=θ). We write the restoring force as θsin mg F -=, where the minus sign indicates that F acts opposite the displacement.(3). If we assume that the angle is small , the θsin is very nearly equal to θ in radians, and the displacement s of the particle measured along its arc is equal to θL . Thus, we have s L mg L s mg mg F )(-=-=-≈θ. Thus if a simple pendulum swings through a small angle, it is a linear oscillator like the block-spring oscillator.(4). Now the amplitude of the motion is measure as the angular amplitude m θ, the maximum angle of swing. Theperiod of a simple pendulum is g L L mg m k m T /2)//(2/2πππ===. Thisresult hods only if the angularamplitude m θ is small .2. The Physical Pendulum(1). The figure shows a generalizedphysical pendulum, as we shall call realistic pendulum , with its weight g m ρ acting at the center of mass C.(2). When the pendulum is displaced through an angle θ in either direction from its equilibrium position, a restoring torque appears. This torque acts about an axis through the suspension point O in the figure and has the magnitude ))(sin (h mg θτ-=. The minus sign indicates that the torque is a restoring torque, which always acts to reduce the angle θ to zero.(3). We once more decide to limit our interest to small amplitude , so thatθθ≈sin . Then the torque becomes θτ)(mgh -=.(4). Thus the period of a physical pendulum ismgh I T /2π=, when m θ is small. Here I is the rotational inertia of thependulum.(5). Corresponding to any physical pendulum that oscillates about a given suspension point O with period T is a simple pendulum of length L 0 with the same period T . The point along the physical pendulum at distance L 0 from point O is called the center of oscillation of the physical pendulum for the given suspension point.3. Measuring g: We can use a physical pendulum to measurethe free-fall acceleration g through measuring the period of the pendulum.9.6 Simple Harmonic Motion and Uniform circular Motion 1.Simple harmonic motion is the projection of uniform circular motion on a diameter of the circle in which the latter motion occurs.2.The figure (a) gives anexample. It shows areference particle P’moving in uniformcircular motion withangular speed ωin areference circle. Theradiusx of the circle ismthe magnitude of theparticle’s position vector.At any time t, theangular position of theparticle is φω+t.3.The projection of particle P’ onto the x axis is a point P. The projection of the position vector of particle P’ onto the x axisgives the location )(t x of P . Thus we find )cos()(φω+=t x t x m . Thus if reference particle P’ moves in uniform circular motion, its projection particle P moves in simple harmonic motion.4. The figure (b) shows the velocity of the reference particle. The magnitude of the velocity ism x ω, and its projection on the x axis is )sin()(φωω+-=t x t v m . The minus sign appears because the velocity component of P points to the left, in the direction of decreasing x .5. The figure (c) shows the acceleration of the reference particle. The magnitude of the acceleration vector ism x 2ω and itsprojection on the x axis is )cos()(2φωω+-=t x t a m . 6. Thus whether we look at the displacement, the velocity, or the acceleration, the projection of uniform circular motion is indeed simple harmonic motion .9.7 Damped Simple Harmonic MotionA pendulum will swing hardly at allunder water, because the water exerts adrag force on the pendulum that quicklyeliminates the motion. A pendulum swinging in air does better, but still themotion dies out because the air exerts a drag force on the pendulum, transferring energy from the pendulum’s motion.1. When the motion of an oscillator is reduced by an external force, the oscillator and its motion are said to be damped . An idealized example of a damped oscillator is shown in the figure: a block with mass m oscillates on a spring with spring constant k. From the mass, a rod extends to a vane (both assumed massless) that is submerged in a liquid. As the vane moves up and down, the liquid exerts an inhibiting drag force on it and thus on the entire oscillating system. With time, the mechanical energy of the block-spring system decreases, as energy is transferred to thermal energy of the liquid and vane.2. Let us assume that the liquid exerts a damped force d F ρthat isproportional in magnitude to the velocity v ρ of the vane and block. Then bv F d -=, where b is a damped constant that depends on the characteristics of both the vane and the liquid and has the SI unit of kilogram per second. The minus sign indicates that d F ρ opposes the motion.3. The total force acting on the block is ∑=-=--=dt dx b kx bv kx F . So we have equation 022=++kx dt dx b dt x d m , whose solution is )'cos()(2/φω+=-t e x t x m bt m , where 'ω, the angular frequency of thedamped oscillator, is given by 224'm b m k -=ω.4. We can regard the displacement of the damped oscillator as a cosine function whose amplitude, which ism bt m e x 2/-, gradually decreases with time.5. The mechanical energy of a damped oscillator is not constant but decreases with time. If the damping is small , we can find )(t E by replacing m x with m bt m e x 2/-, the amplitude of thedamped oscillation. Doing so, we findm bt m e kx t E /221)(-≈, which tells us that the mechanical energy decreases exponentially with time .9.8 Forced Oscillations and Resonance1. A person swing passivelyin a swing is an example offree oscillation. If a kindfriend pulls or pushes theswing periodically, as in thefigure, we have forced, ordriven, oscillations . There are now two angular frequencies with which to deal with: (1) the natural angular frequency ω of the system, which is the angular frequency at which it would oscillate if it were suddenly disturbed and then left tooscillate freely, and (2) the angular frequencyd ω of theexternal driving force.2. We can use the right figure torepresent an idealized forcedsimple harmonic oscillator if weallow the structure marked “rigidsupport” to move up and down ata variable angular frequency d ω. A forced oscillator oscillates atthe angular frequencyd ω of driving force, and its displacement is given by )cos()(φω+=t x t x d m , where m xis the amplitude of the oscillations. How large the displacement amplitudem x is depends on a complicated function of d ω and ω.3. The velocity amplitude m v of the oscillations is easier todescribe: it is greatest whenωω=d , a condition calledresonance . Above equationis also approximately thecondition at which thedisplacement amplitude m xof oscillations is greatest.The figure shows how the displacement amplitude of an oscillator depends on the angular frequencyof thed driving force, for three values of the damped coefficient b. 4.All mechanical structures have one or more naturalfrequencies, and if a structure is subjected to a strong external driving force that matches one of these frequencies, the resulting oscillations of structure may rupture it. Thus, for example, aircraft designers must make sure that none of the natural frequencies at which a wing can vibrate matches the angular frequency of the engines at cruising speed.。
