Educatione Ducation

Total Page:16

File Type:pdf, Size:1020Kb

Educatione Ducation EDUCATIONE DUCATION Editor: Denis Donnelly, [email protected] THE FAST FOURIER TRANSFORM FOR EXPERIMENTALISTS PART III: CLASSICAL SPECTRAL ANALYSIS By Bert Rust and Denis Donnelly ACH ARTICLE IN THIS CONTINUING SERIES ON THE FAST Pf()= 1 T 2 FOURIER TRANSFORM (FFT) IS DESIGNED TO ILLUMINATE lim∫ xt ( ) exp(−2πi ftdt ) , E T →∞T −T NEW FEATURES OF THE WIDE-RANGING APPLICABILITY OF THIS –¥£f £¥.(3) TRANSFORM. THIS SEGMENT DEALS WITH SOME ASPECTS OF THE But we have only a discrete, real time series spectrum estimation problem. Before Spectrum Estimation’s ⌬ we begin, here’s a short refresher Central Problem xj = x(tj), with tj = j t, about two elements we introduced The periodogram, invented by j = 0, 1, …, N – 1, (4) previously, windowing1 and convolu- Arthur Schuster in 1898,3 was the tion.2 As we noted in those install- first formal estimator for a time se- defined on a finite time interval of ments, a convolution is an integral ries’s frequency spectrum, but many length N⌬t. We saw in Part I1 that that expresses the amount of overlap others have emerged in the ensuing sampling x(t) with sample spacing ⌬t of one function as it is shifted over an- century. Almost all use the FFT in confined our spectral estimates to the other. The result is a blending of the their calculations, but they differ in Nyquist band 0 £ f £ 1/2⌬t. We used two functions. Closely related to the their assumptions about the missing the FFT algorithm to compute the dis- convolution process are the processes data; that is, the data outside the ob- crete Fourier transform (DFT) of cross-correlation and autocorrela- servation window. These assumptions tion. Computing the cross-correlation have profound effects on the spectral N −1 =−π j differs only slightly from the convolu- estimates. Let t be time, f be fre- Xxkj∑ exp 2 i k = N tion; it’s useful for finding the degree quency, and x(t) a real function on the j 0 of similarity in signal patterns from interval – < t < ¥. The continuous k = 0, 1, …, N/2, (5) two different data streams and in de- Fourier transform (CFT) of x(t) is de- termining the lead or lag between fined by which approximates the CFT X(f ) at such similar signals. Autocorrelation the Fourier frequencies ∞ is also related to the convolution; it’s X ()fxtftdt=−∫ ()exp(2πi ) , k described later. Windowing, used in −∞ f = , k = 0, 1, ..., N/2. (6) k Nt∆ extracting or smoothing data, is typi- cally executed by multiplying time- –¥£f £¥,(1)We then computed periodogram esti- domain data or its autocorrelation mates of both the PSD and the ampli- function by the window function. A where i ≡−1. If we knew x(t) per- tude spectrum by disadvantage of windowing is that it fectly and could compute Equation 1, alters or restricts the data, which, of then we could compute an energy = 1 2 Pf()kk||X , k = 0, 1, …, N/2, course, has consequences for the spec- spectral density function N tral estimate. In this installment, we continue our discussion, building on E(f ) = |X(f )|2, –¥£f £¥,(2) = 2 Af()kk||X , k = 0, 1, …, N/2. (7) these concepts with a more general N approach to computing spectrum es- and a power spectral density function timates via the FFT. (PSD) by We also saw that we could approximate 74 Copublished by the IEEE CS and the AIP 1521-9615/05/$20.00 © 2005 IEEE COMPUTING IN SCIENCE & ENGINEERING x(t) = sin [2π(0.50)(t + 0.25)] + noise 2.5 2.0 Signal and noise 1.5 Signal without noise ) i t ( 1.0 x 0.5 = i x 0.0 –0.5 –1.0 –1.5 the CFT and the frequency spectrum 0.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 on a denser frequency mesh simply by (a) t (time units) appending zeroes to the time series. Periodogram This practice, called zero padding, is 9 just an explicit assertion of an implicit 8 Signal and noise 7 Signal without noise assumption of the periodogram 6 method—namely, that the time series 5 4 is zero outside the observation window. 3 density (PSD) 2 Frequency spectrum estimation is a Power spectral 1 classic underdetermined problem be- 0 cause we need to estimate the spectrum 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0 2.2 2.4 at an infinite number of frequencies us- (b) Frequency ing only a finite amount of data. This problem has many solutions, differing Figure 1. Original and new time series as defined by Equation 8. (a) The noise- mainly in what they assume about the corrupted time series and the uncorrupted series originally used in Part I’s Figure 1b. missing data. The noise is independently, identically distributed n(0, 0.25). (b) Periodograms of Before considering other solutions the two times series plotted in (a). For the noise-corrupted series, the peak is ^ to this problem, let’s reconsider one of centered on frequency f 0 = 0.493. the examples from Part I1 (specifically, Figure 1b), but make it more realistic 4 ␶ ⌬ by simulating some random measure- Tukey’s correlogram estimators. m = m t, m = 0, 1, …, N – 1. (11) ment errors. More precisely, we take They’re based on the autocorrelation N = 32, ⌬t = 0.22, and consider the theorem (sometimes called Wiener’s Because we’re working with a real time ␳ ␶ ␳ ␶ time series theorem), which states that if X(f ) is series, and ( –m) = ( m), we don’t need the CFT of x(t), then |X(f )|2 is the to worry about evaluating ␳(␶) at neg- ⌬ tj = j t, j = 0, 1, 2, …, N – 1, CFT of the autocorrelation function ative lags. (ACF) of x(t). Norbert Wiener defined Because ␳(␶) is a limit of the average ␲ Î 5 ␶ xj = x(tj) = sin[2 f0(tj + 0.25)] + j, (8) the latter function as value of x*(t)x(t + ) on the interval [–T, T ], the obvious estimator is the 1 T with f = 0.5, and each Î a random ρτ( )=+ lim∫ xtxtdt * ( ) ( τ ) , sequence of average values 0 j T →∞ T −T number drawn independently from a 2 ρρˆˆ=∆()mt normal distribution with mean zero –¥ < ␶ < ¥, (9) m −− and standard deviation ␴ = 0.25. This 1 Nm1 = ∑ xx new time series is plotted together in which the variable ␶ is called the lag − nnm+ , Nmn=0 with the original uncorrupted series in (the time interval for the correlation of Figure 1a. Both series were zero x(t) with itself), and x*(t) is the com- m = 0, 1, …, N – 1. (12) padded to length 1,024 (992 zeroes plex conjugate of x(t). Thus, if we appended) to obtain the periodogram could access x(t), we could compute This sequence is sometimes called the estimates given in Figure 1b. It’s re- the PSD in two ways: either by Equa- unbiased estimator of ␳(␶) because its markable how well the two spectra tion 3 or by expected value is the true value—that ^ agree, even though the noise’s stan- ∞ is, ⑀{␳(m⌬t)} = ␳(m⌬t). But the data =−ρτ π τ τ. (10) dard deviation was 25 percent of the P()ffd∫−∞ ()exp(2 i ) are noisy, and for successively larger ␳^ signal’s amplitude. values of m, the average m is based on But again, we have access to only a fewer and fewer terms, so the variance The Autocorrelation Function noisy time series x0, x1, …, xN–1, so to grows and, for large m, the estimator After the periodogram, the next fre- use the second method, we need esti- becomes unstable. Therefore, it’s quency spectrum estimators to emerge mates for ␳(␶) evaluated at the discrete common practice to use the biased were Richard Blackman and John lag values estimator SEPTEMBER/OCTOBER 2005 75 E DUCATION Autocorrelation estimates 1.0 0.8 Biased estimate 0.6 Unbiased estimate 0.4 t) ∆ 0.2 m ( 0.0 ␳ –0.2 –0.4 –0.6 0.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 corresponding to the biased and un- (a) Lag = m∆t biased ACF estimates, shown in Fig- Correlogram estimates of power spectral density (PSD) ure 2a. The negative sidelobes for the unbiased correlogram show dramati- 14 Using biased ACF cally why most analysts choose the bi- 10 Using unbiased ACF ased estimate even though its central 6 ) f peak is broader. The reason for this 2 P( broadening, and for the damped side- –2 lobes, is that the biased ACF, Equa- –6 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0 2.2 2.4 tion 13, can also be computed by multiplying the unbiased ACF, Equa- (b) f tion 12, by the triangular (Bartlett) ta- pering window Figure 2. Autocorrelation and correlogram estimates for the noisy time series k defined by Equation 8. (a) Biased and unbiased estimates of the autocorrelation w =−1 , k N function (ACF); (b) correlogram estimates obtained from the ACF estimates in (a). k = 0, 1, 2, …, N – 1. (17) Nm−−1 Recall that we observed the same sort ρρ=∆=1 £ ^ £ ˆˆmnnm()mt ∑ xx+ , –1 rm 1, m = 0, 1, …, N – 1. (15) of peak broadening and sidelobe sup- N n=0 pression in Part I’s Figure 10 when we m = 0, 1, …, N – 1, (13) Correlogram PSD Estimators multiplied the observed data by a Once we’ve established the ACF esti- Blackman window before computing which damps those instabilities and has mate, we can use the FFT to calculate the periodogram.
Recommended publications
  • Configuring Spectrogram Views
    Chapter 5: Spectrographic Analysis Configuring spectrographic views To create a new spectrogram, spectrogram slice, or selection spectrum view, click on the appropriate New View button in the view toolbar (Figure 5.3) or choose a view type from the View > New menu. Figure 5.3. New View buttons Figure 5.3. The New View buttons, in Raven’s view toolbar. 112 Raven Pro 1.4 User’s Manual Chapter 5: Spectrographic Analysis A dialog box appears, containing parameters for configuring the requested type of spectrographic view (Figure 5.4). The dialog boxes for configuring spectrogram and spectrogram slice views are identical, except for their titles. The dialog boxes are identical because both view types calculate a spectrogram of the entire sound; the only difference between spectrogram and spectrogram slice views is in how the data are displayed (see “How the spectrographic views are related” on page 110). The dialog box for configuring a selection spectrum view is the same, except that it lacks the Averaging parameter. The remainder of this section explains each of the parameters in the configuration dialog box. Figure 5.4 Configure Spectrogram dialog Figure 5.4. The Configure New Spectrogram dialog box. Window type Each data record is multiplied by a window function before its spectrum is calculated. Window functions are used to reduce the magnitude of spurious “sidelobe” energy that appears at frequencies flanking each analysis frequency in a spectrum. These sidelobes appear as a result of Raven Pro 1.4 User’s Manual 113 Chapter 5: Spectrographic Analysis analyzing a finite (truncated) portion of a signal.
    [Show full text]
  • Windowing Techniques, the Welch Method for Improvement of Power Spectrum Estimation
    Computers, Materials & Continua Tech Science Press DOI:10.32604/cmc.2021.014752 Article Windowing Techniques, the Welch Method for Improvement of Power Spectrum Estimation Dah-Jing Jwo1, *, Wei-Yeh Chang1 and I-Hua Wu2 1Department of Communications, Navigation and Control Engineering, National Taiwan Ocean University, Keelung, 202-24, Taiwan 2Innovative Navigation Technology Ltd., Kaohsiung, 801, Taiwan *Corresponding Author: Dah-Jing Jwo. Email: [email protected] Received: 01 October 2020; Accepted: 08 November 2020 Abstract: This paper revisits the characteristics of windowing techniques with various window functions involved, and successively investigates spectral leak- age mitigation utilizing the Welch method. The discrete Fourier transform (DFT) is ubiquitous in digital signal processing (DSP) for the spectrum anal- ysis and can be efciently realized by the fast Fourier transform (FFT). The sampling signal will result in distortion and thus may cause unpredictable spectral leakage in discrete spectrum when the DFT is employed. Windowing is implemented by multiplying the input signal with a window function and windowing amplitude modulates the input signal so that the spectral leakage is evened out. Therefore, windowing processing reduces the amplitude of the samples at the beginning and end of the window. In addition to selecting appropriate window functions, a pretreatment method, such as the Welch method, is effective to mitigate the spectral leakage. Due to the noise caused by imperfect, nite data, the noise reduction from Welch’s method is a desired treatment. The nonparametric Welch method is an improvement on the peri- odogram spectrum estimation method where the signal-to-noise ratio (SNR) is high and mitigates noise in the estimated power spectra in exchange for frequency resolution reduction.
    [Show full text]
  • Windowing Design and Performance Assessment for Mitigation of Spectrum Leakage
    E3S Web of Conferences 94, 03001 (2019) https://doi.org/10.1051/e3sconf/20199403001 ISGNSS 2018 Windowing Design and Performance Assessment for Mitigation of Spectrum Leakage Dah-Jing Jwo 1,*, I-Hua Wu 2 and Yi Chang1 1 Department of Communications, Navigation and Control Engineering, National Taiwan Ocean University 2 Pei-Ning Rd., Keelung 202, Taiwan 2 Bison Electronics, Inc., Neihu District, Taipei 114, Taiwan Abstract. This paper investigates the windowing design and performance assessment for mitigation of spectral leakage. A pretreatment method to reduce the spectral leakage is developed. In addition to selecting appropriate window functions, the Welch method is introduced. Windowing is implemented by multiplying the input signal with a windowing function. The periodogram technique based on Welch method is capable of providing good resolution if data length samples are selected optimally. Windowing amplitude modulates the input signal so that the spectral leakage is evened out. Thus, windowing reduces the amplitude of the samples at the beginning and end of the window, altering leakage. The influence of various window functions on the Fourier transform spectrum of the signals was discussed, and the characteristics and functions of various window functions were explained. In addition, we compared the differences in the influence of different data lengths on spectral resolution and noise levels caused by the traditional power spectrum estimation and various window-function-based Welch power spectrum estimations. 1 Introduction knowledge. Due to computer capacity and limitations on processing time, only limited time samples can actually The spectrum analysis [1-4] is a critical concern in many be processed during spectrum analysis and data military and civilian applications, such as performance processing of signals, i.e., the original signals need to be test of electric power systems and communication cut off, which induces leakage errors.
    [Show full text]
  • Discrete-Time Fourier Transform
    .@.$$-@@4&@$QEG'FC1. CHAPTER7 Discrete-Time Fourier Transform In Chapter 3 and Appendix C, we showed that interesting continuous-time waveforms x(t) can be synthesized by summing sinusoids, or complex exponential signals, having different frequencies fk and complex amplitudes ak. We also introduced the concept of the spectrum of a signal as the collection of information about the frequencies and corresponding complex amplitudes {fk,ak} of the complex exponential signals, and found it convenient to display the spectrum as a plot of spectrum lines versus frequency, each labeled with amplitude and phase. This spectrum plot is a frequency-domain representation that tells us at a glance “how much of each frequency is present in the signal.” In Chapter 4, we extended the spectrum concept from continuous-time signals x(t) to discrete-time signals x[n] obtained by sampling x(t). In the discrete-time case, the line spectrum is plotted as a function of normalized frequency ωˆ . In Chapter 6, we developed the frequency response H(ejωˆ ) which is the frequency-domain representation of an FIR filter. Since an FIR filter can also be characterized in the time domain by its impulse response signal h[n], it is not hard to imagine that the frequency response is the frequency-domain representation,orspectrum, of the sequence h[n]. 236 .@.$$-@@4&@$QEG'FC1. 7-1 DTFT: FOURIER TRANSFORM FOR DISCRETE-TIME SIGNALS 237 In this chapter, we take the next step by developing the discrete-time Fouriertransform (DTFT). The DTFT is a frequency-domain representation for a wide range of both finite- and infinite-length discrete-time signals x[n].
    [Show full text]
  • Understanding the Lomb-Scargle Periodogram
    Understanding the Lomb-Scargle Periodogram Jacob T. VanderPlas1 ABSTRACT The Lomb-Scargle periodogram is a well-known algorithm for detecting and char- acterizing periodic signals in unevenly-sampled data. This paper presents a conceptual introduction to the Lomb-Scargle periodogram and important practical considerations for its use. Rather than a rigorous mathematical treatment, the goal of this paper is to build intuition about what assumptions are implicit in the use of the Lomb-Scargle periodogram and related estimators of periodicity, so as to motivate important practical considerations required in its proper application and interpretation. Subject headings: methods: data analysis | methods: statistical 1. Introduction The Lomb-Scargle periodogram (Lomb 1976; Scargle 1982) is a well-known algorithm for de- tecting and characterizing periodicity in unevenly-sampled time-series, and has seen particularly wide use within the astronomy community. As an example of a typical application of this method, consider the data shown in Figure 1: this is an irregularly-sampled timeseries showing a single ob- ject from the LINEAR survey (Sesar et al. 2011; Palaversa et al. 2013), with un-filtered magnitude measured 280 times over the course of five and a half years. By eye, it is clear that the brightness of the object varies in time with a range spanning approximately 0.8 magnitudes, but what is not immediately clear is that this variation is periodic in time. The Lomb-Scargle periodogram is a method that allows efficient computation of a Fourier-like power spectrum estimator from such unevenly-sampled data, resulting in an intuitive means of determining the period of oscillation.
    [Show full text]
  • Airborne Radar Ground Clutter Suppression Using Multitaper Spectrum Estimation Comparison with Traditional Method
    Airborne Radar Ground Clutter Suppression Using Multitaper Spectrum Estimation Comparison with Traditional Method Linus Ekvall Engineering Physics and Electrical Engineering, master's level 2018 Luleå University of Technology Department of Computer Science, Electrical and Space Engineering Airborne Radar Ground Clutter Suppression Using Multitaper Spectrum Estimation & Comparison with Traditional Method Linus C. Ekvall Lule˚aUniversity of Technology Dept. of Computer Science, Electrical and Space Engineering Div. Signals and Systems 20th September 2018 ABSTRACT During processing of data received by an airborne radar one of the issues is that the typical signal echo from the ground produces a large perturbation. Due to this perturbation it can be difficult to detect targets with low velocity or a low signal-to-noise ratio. Therefore, a filtering process is needed to separate the large perturbation from the target signal. The traditional method include a tapered Fourier transform that operates in parallel with a MTI filter to suppress the main spectral peak in order to produce a smoother spectral output. The difference between a typical signal echo produced from an object in the environment and the signal echo from the ground can be of a magnitude corresponding to more than a 60 dB difference. This thesis presents research of how the multitaper approach can be utilized in concurrence with the minimum variance estimation technique, to produce a spectral estimation that strives for a more effective clutter suppression. A simulation model of the ground clutter was constructed and also a number of simulations for the multitaper, minimum variance estimation technique was made. Compared to the traditional method defined in this thesis, there was a slight improve- ment of the improvement factor when using the multitaper approach.
    [Show full text]
  • The Sliding Window Discrete Fourier Transform Lee F
    The Sliding Window Discrete Fourier Transform Lee F. Richardson Department of Statistics and Data Science, Carnegie Mellon University, Pittsburgh, PA The Sliding Window DFT Sliding Window DFT for Local Periodic Signals Leakage The Sliding Window Discrete Fourier Transform (SW- Leakage occurs when local signal frequency is not one DFT) computes a time-frequency representation of a The Sliding Window DFT is a useful tool for analyzing data with local periodic signals (Richardson and Eddy of the Fourier frequencies (or, the number of complete signal, and is useful for analyzing signals with local (2018)). Since SW-DFT coefficients are complex-numbers, we analyze the squared modulus of these coefficients cycles in a length n window is not an integer). In this periodicities. Shortly described, the SW-DFT takes 2 2 2 case, the largest squared modulus coefficients occur at sequential DFTs of a signal multiplied by a rectangu- |ak,p| = Re(ak,p) + Im(ak,p) frequencies closest to the true frequency: lar sliding window function, where the window func- Since the squared modulus SW-DFT coefficients are large when the window is on a periodic part of the signal: Frequency: 2 Frequency: 2.1 Frequency: 2.2 Frequency: 2.3 Frequency: 2.4 20 Freq 2 20 Freq 2 20 Freq 2 20 Freq 2 20 Freq 2 tion is only nonzero for a short duration. Let x = Freq 3 Freq 3 Freq 3 Freq 3 Freq 3 15 15 15 15 15 [x0, x1, . , xN−1] be a length N signal, then for length 10 10 10 10 10 n ≤ N windows, the SW-DFT of x is: 5 5 5 5 5 0 0 0 0 0 0 50 100 150 200 250 0 50 100 150 200 250 0 50 100 150 200 250 0 50 100 150 200 250 0 50 100 150 200 250 1 n−1 X −jk Frequency: 2.5 Frequency: 2.6 Frequency: 2.7 Frequency: 2.8 Frequency: 2.9 ak,p = √ xp−n+1+jω 20 Freq 2 20 Freq 2 20 Freq 2 20 Freq 2 20 Freq 2 n n Freq 3 Freq 3 Freq 3 Freq 3 Freq 3 j=0 15 15 15 15 15 k = 0, 1, .
    [Show full text]
  • Topics in Time-Frequency Analysis
    Kernels Multitaper basics MT spectrograms Concentration Topics in time-frequency analysis Maria Sandsten Lecture 7 Stationary and non-stationary spectral analysis February 10 2020 1 / 45 Kernels Multitaper basics MT spectrograms Concentration Wigner distribution The Wigner distribution is defined as, Z 1 τ ∗ τ −i2πf τ Wz (t; f ) = z(t + )z (t − )e dτ; −∞ 2 2 with t is time, f is frequency and z(t) is the analytic signal. The ambiguity function is defined as Z 1 τ ∗ τ −i2πνt Az (ν; τ) = z(t + )z (t − )e dt; −∞ 2 2 where ν is the doppler frequency and τ is the lag variable. 2 / 45 Kernels Multitaper basics MT spectrograms Concentration Ambiguity function Facts about the ambiguity function: I The auto-terms are always centered at ν = τ = 0. I The phase of the ambiguity function oscillations are related to centre t0 and f0 of the signal. I jAz1 (ν; τ)j = jAz0 (ν; τ)j when Wz1 (t; f ) = Wz0 (t − t0; f − f0). I The cross-terms are always located away from the centre. I The placement of the cross-terms are related to the interconnection of the auto-terms. 3 / 45 Kernels Multitaper basics MT spectrograms Concentration The quadratic class The quadratic or Cohen's class is usually defined as Z 1 Z 1 Z 1 Q τ ∗ τ i2π(νt−f τ−νu) Wz (t; f ) = z(u+ )z (u− )φ(ν; τ)e du dτ dν: −∞ −∞ −∞ 2 2 The filtered ambiguity function can also be formulated as Q Az (ν; τ) = Az (ν; τ) · φ(ν; τ); and the corresponding smoothed Wigner distribution as Q Wz (t; f ) = Wz (t; f ) ∗ ∗Φ(t; f ); using the corresponding time-frequency kernel.
    [Show full text]
  • Fourier Analysis
    FOURIER ANALYSIS Lucas Illing 2008 Contents 1 Fourier Series 2 1.1 General Introduction . 2 1.2 Discontinuous Functions . 5 1.3 Complex Fourier Series . 7 2 Fourier Transform 8 2.1 Definition . 8 2.2 The issue of convention . 11 2.3 Convolution Theorem . 12 2.4 Spectral Leakage . 13 3 Discrete Time 17 3.1 Discrete Time Fourier Transform . 17 3.2 Discrete Fourier Transform (and FFT) . 19 4 Executive Summary 20 1 1. Fourier Series 1 Fourier Series 1.1 General Introduction Consider a function f(τ) that is periodic with period T . f(τ + T ) = f(τ) (1) We may always rescale τ to make the function 2π periodic. To do so, define 2π a new independent variable t = T τ, so that f(t + 2π) = f(t) (2) So let us consider the set of all sufficiently nice functions f(t) of a real variable t that are periodic, with period 2π. Since the function is periodic we only need to consider its behavior on one interval of length 2π, e.g. on the interval (−π; π). The idea is to decompose any such function f(t) into an infinite sum, or series, of simpler functions. Following Joseph Fourier (1768-1830) consider the infinite sum of sine and cosine functions 1 a0 X f(t) = + [a cos(nt) + b sin(nt)] (3) 2 n n n=1 where the constant coefficients an and bn are called the Fourier coefficients of f. The first question one would like to answer is how to find those coefficients.
    [Show full text]
  • Understanding FFT Overlap Processing Fundamentals
    Understanding FFT Overlap Processing Fundamentals Primer Primer Contents Introduction ....................................................................3 Amplitude Effects .........................................................14 The need for seeing faster time-varying signals .................3 Brief pulses .....................................................................14 The Kaiser Window .........................................................17 Expand the Spectrogram View ......................................4 Calculating Amplitude Reduction .....................................17 How Overlap works ........................................................5 Other windows ................................................................18 Overlapping many Time-to-Frequency transforms .............5 Super-short pulses ..........................................................18 Spectrum and Spectrogram comparisons ....................7 Gaps in Time Lose Signals ..........................................20 Non-overlapped transform processing ..............................7 Actual gaps .....................................................................20 Overlapped time transform processing ..............................7 Effective gaps .................................................................20 Stretching time and impact on Spectrum view ..................8 Overlap – with no gaps ...................................................21 Example: a pseudo-random modulated pulse ...................9 Digital Phosphor Spectrum
    [Show full text]
  • Short-Window Spectral Analysis Using AMVAR and Multitaper Methods: a Comparison
    Biol Cybern (2009) 101:71–80 DOI 10.1007/s00422-009-0318-5 ORIGINAL PAPER Short-window spectral analysis using AMVAR and multitaper methods: a comparison Hariharan Nalatore · Govindan Rangarajan Received: 6 October 2008 / Accepted: 6 May 2009 / Published online: 27 May 2009 © Springer-Verlag 2009 Abstract We compare two popular methods for estimat- even over time intervals of the order of one second, one ing the power spectrum from short data windows, namely must examine them using short time windows of 50–100ms the adaptive multivariate autoregressive (AMVAR) method duration or even shorter. The theoretical basis for divid- and the multitaper method. By analyzing a simulated sig- ing the signal into short time windows lies in treating the nal (embedded in a background Ornstein–Uhlenbeck noise time series within such short windows as being generated process) we demonstrate that the AMVAR method performs by an approximately stationary stochastic process. In other better at detecting short bursts of oscillations compared to words, the full time series is viewed as being generated by the multitaper method. However, both methods are immune a non-stationary stochastic process with locally stationary to jitter in the temporal location of the signal. We also show segments. that coherence can still be detected in noisy bivariate time For the 200Hz sampling frequency used in our study, a series data by the AMVAR method even if the individual duration of 50–100ms corresponds to a data string of only power spectra fail to show any peaks. Finally, using data 11–21 points. The conventional non-parametric approach to from two monkeys performing a visuomotor pattern discrim- spectral analysis, based on the discrete Fourier transform, ination task, we demonstrate that the AMVARmethod is bet- is not effective for such short data lengths.
    [Show full text]
  • Some Notes on the Use of the Windowed Fourier Transform for Spectral Analysis of Discretely Sampled Data
    Submitted to Axioms. Pages 1 - 24. OPEN ACCESS axioms ISSN 2075-1680 www.mdpi.com/journal/axioms Article Some Notes on the Use of the Windowed Fourier Transform for Spectral Analysis of Discretely Sampled Data Robert W. Johnson Alphawave Research, 29 Stanebrook Ct., Jonesboro, GA 30238, USA Version May 23, 2013 submitted to Axioms. Typeset by LATEX using class file mdpi.cls Abstract: The properties of the Gabor and Morlet transforms are examined with respect to the Fourier analysis of discretely sampled data. Forward and inverse transform pairs based on a fixed window with uniform sampling of the frequency axis can satisfy numerically the energy and reconstruction theorems; however, transform pairs based on a variable window or nonuniform frequency sampling in general do not. Instead of selecting the shape of the window as some function of the central frequency, we propose constructing a single window with unit energy from an arbitrary set of windows which is applied over the entire frequency axis. By virtue of using a fixed window with uniform frequency sampling, such a transform satisfies the energy and reconstruction theorems. The shape of the window can be tailored to meet the requirements of the investigator in terms of time/frequency resolution. The algorithm extends naturally to the case of nonuniform signal sampling without modification beyond identification of the Nyquist interval. Keywords: Fourier transform, Gabor transform, Morlet transform, multiresolution analysis 1. Introduction arXiv:1303.0306v2 [physics.data-an] 22 May 2013 The primary criticism leveled at the use of the continuous wavelet transform for the spectral analysis of discretely sampled data is that it fails to give quantitatively meaningful results.
    [Show full text]