Presentation on theme: "Heriot-Watt, School of Engineering & Physical Sciences"— Presentation transcript:
1Heriot-Watt, School of Engineering & Physical Sciences One, Two or Many Frequencies: Synchrosqueezing and Multicomponent Signal AnalysisSteve McLaughlinHeriot-Watt, School of Engineering & Physical Scienceswith thanks toPatrick Flandrin, Mike Davies, Yannis Kopsinis, Sylvain Meignen, Thomas Oberlin
4StructureIntroductionSignals, Time and FrequencyEmpirical Mode DecompositionSynchroSqueezing
5What are signals?A signal is a time (or space) varying quantity that can carry information.The concept is broad, and hard to define precisely.(Wikipedia)
6What are signals made of? The Frequency viewpoint (Fourier):Joseph FourierSignals can be built from the sum of harmonic functions (sine waves)Continuous to discreteNyquist/Shannon Sampling TheoremRepresenting signals as impulses or sums of sinusoidssignalHarmonic functionsFourier coefficients
7What is a Signal?Theintrinsic dynamics of the vocal folds can produce complextemporal acoustic call patterns without complex nervoussystem controlW. Tecumseh Fitch, “Calls out of chaos: the adaptive significance of nonlinear phenomena in mammalian vocal production”, Animal Behaviour, 2002
8What is a SignalFrequency modulation (FM) implies the instantaneous frequency varies. This contrasts with amplitude modulation, in which it is the amplitude which is varied while its frequency remains constant. Many signals can be modeled as a sum of AM and FM components.
9Empirical Mode Decomposition Basic idea is very simple:signal = fast oscillation + slow oscillation(& iteration)- Separation “fast vs slow’ data drivenLocal analysis based on neighbouring extremaOscillation rather than frequency
10Empirical Mode Decomposition Identify local maxima and local minimaDeduce an upper envelope and a lower envelope by interpolation (cubic splines)subtract the mean envelope from the signaliterate until “mean envelope = 0" (sifting)subtract the obtained mode from the signaliterate on the residual
12EMD - SummaryLocality — The method operates at the scale of one oscillationAdaptive — The decomposition is fully data-drivenMulti-resolution — The iterative process explores sequentially the “natural” scales of a signalOscillations of any type — No assumption on the nature of oscillations (e.g., harmonic) ⇒ 1 nonlinear oscillation = 1 modeNo analytic definition — The decomposition is only defined as the output of an algorithm ⇒ analysis and evaluation ?
13What is frequency?Frequency is the number of occurrences of a repeating event per unit time. It is also referred to as temporal frequency. The period is the duration of one cycle in a repeating event, so the period is the reciprocal of the frequency.(Wikipedia)
14What is frequency?MacaqueFatima MirandaAmolops tormotus
15Hilbert SpectrumCan we produce TF-representation plots based on the IF, IA estimates?Hilbert Spectrum: IA and IF are combined in order to form a spectrogram-like plot, H(t,f ).Specifically, for any time instant t, H(t,f )=α(t) if IF(t) equals to f and zero at the rest of the frequencies.In practice, a Time – Frequency grid is adopted in order to construct H(t,f ).Steve, Hilbert spectrum was initially used for plotting the IF of the IMFs produced by EMD.
16On Instantaneous Frequency (Monocomponent signals) Steve, on the left we see a linear chirp going from frequency 90KHz down to 55. This chirp is also modulated by a sinusoid. The Frequency of the sinusoid for the amplitude modulation is much lower than the frequency of chirp. So the signal can be characterised as monocomponent.In the case of monocomponent signals, IF coincides with the actual frequency of the signal.
17Instantaneous Frequency Multicomponent signals Steve,Fig a1: Fist component – it is a linear chirp;Fig a2: Second component – it is a linear chirp parallel to the one of Fig a1;Fig a3: Sum of the two components.Fig b1: Hilbert spectrum of the signal (shown in Fig a3). The green dashed lines shows the actual frequency of the two components (of Fig a1 and Fig a2).Fig b2: Shows the Hilbert spectrum of the unweighted averaged IF.Fig b3: Shows the IA which is used for the production of the Hilbert spectrum.(See also discussion on page 3 left column, last paragraph)IF is a non-symmetric oscillation exhibiting spikes that point toward the component with the larger amplitude.The strength of the IF oscillations (that is the height of the spikes) depends on the relative amplitudes of the components.Smoothing, , forces IF to “lock” at the component with the higher amplitude
18Instantaneous Frequency Multicomponent signals The further apart in frequency the components are, the larger the amplitude of oscillations become. The frequency of oscillations is relative to the frequency difference.
19BeatingWhen a signal is composed of two components with close instantaneous frequencies, EMD exhibits a beating phenomenon [Rilling and Flandrin (2008)].More precisely, if a signal is composed of two harmonics, i.e., f(t) = cos(2πt)+a cos(2πξ0t), Rillingand Flandrin  show an interesting zone in the a vs. ξ plane (amplitude vs. frequency plane) where EMD misidentifies the sum of two components as only a single component; the precise shape of this zone depends on the value ξ0.They also quantified this phenomenon and termed it beating, because EMD “identified” the two harmonics as a single oscillating (i.e., beating) signal.
20Time Frequency Reassignment and SSQ Time-Frequency Reassignment originates from a study of the STFT, which smears the energy of the superimposed Instantaneous Frequencies around their center frequencies in the spectrogram. TFR analysis “reassigns” these energies to sharpen the spectrogramSynchrosqueezing is a special case of reassignment methods [Auger and Flandrin (1995); Chassande–Mottin et al. (2003,1997)].SSQ reallocates the coefficients resulting from a continuous wavelet transform based on the frequency information, to get a concentrated picture over the time-frequency plane, from which the instantaneous frequencies are then extractedIt is adaptive to the signal;Reconstruction of the signal from the reallocated coefficients is feasibleSolid Theoretical foundations
21Idea from Daubechies & Maes 1996 SynchroSqueezingIdea from Daubechies & Maes 1996Concentrate (squeeze) wavelet coefficients, at fixed times, on the basis of local frequency estimation (synchronised)Guarantees a sharply localised representationAllows for reconstruction of identified modesOffers a mathematical tractable alternative to EMD
22Synchrosqueezing – Reconstruction Approach We are interested in the retrieval of the components fk of a multicomponent signal f defined by:The problem can be viewed from two perspectives.The first consists of computing an approximation of the modes, either by using EMD or via wavelet projections.The second uses the SST to reallocate the WT before proceeding with multicomponent retrieval
23Underlying theoryFirst recall some results first proposed by Daubechies et al For a signal defined as a superposition of intrinsic-mode-type functions (IMT):
24Underlying theoryWhere d is a separation condition.
27Theorem – A RemarkComponent retrieval using the SST is thus first based on the computation of the synchrosqueezing operator and then on its integration in the vicinity of curves defined by
28Retrieval of Multiple Components (RCM): Practical Implemetation (Brevdo et al 2011) The strategy developed by Brevdo et al for the RCM is to proceed on a component by component basis. The idea is to find a curve ,in the time-frequency plane such that it maximizes the energy which forces the modes to be smooth through a total variation term penalization and is obtained by computing the following quantity:
29SST Parameters - WT of Pure Harmonic Signals When dealing with a signal made of pure harmonicsThe separation of the components using the WT is equivalent to the support of the WT of each component being disjoint that is for all k>1 :Finally, using the definition of the separation condition introduced in Definition 2, when there is no interference between the components and
30SST Parameters - WT of an ε-IMT Let f(t) be a mode as defined in Definition 2, then the WT of f is now approximated by:the approximation error being controlled by (C1(f)B1+C2(f)B2+(C3(f)B3), (see Brevdo et al 2011), where Ci, 1≤i≤3, does not depend on the wavelet choice, whileSo, when is high, a low approximation error requires thatmust be small. One way to ensure this is to impose that has afast decay or equivalently, that be large.
31SST Parameters - WT of a Superposition of ε-IMT For a multi-component signal as defined in Definition 2 we wish to have the following approximation:So, following the study on a single component, a low approximation error requiresthat be large enough. Then, we also want each component to be well separated from the other components (which is compulsory if we are to use the WT for mode retrieval), that is for each (a,t) the sum above should be reduced to a single term which is true provided that:Taking into account the separation condition (see Definition 2), we can deduce that the above inequality is true as soon as
33Related WorkIt is worth noting that the behavior of the SST was studied in detail by Wu et al 2011 for two tone signals. However, no conclusions can be drawn from the latter regarding modulated signals because the modulation considerably alters the wavelet representation of such signals and consequently the results given by the SST.A very similar study was carried out by Mallat, ([14, p. 102]), but using a mother wavelet which was symmetric and compactly supported in the time domain. In such a case, the second order terms in the approximation would be negligible only if for all k:provided
34ObservationThis second constraint when applied to an -IMT implies that Φk is such that ,provided that , which is true when,or equivalentlyIf we assume that both Ak and are bounded below, the quality of the approximation is better if is small when is large. Since one must also have (where is the frequency bandwidth of the wavelet) to ensure separation of a different component, cannot be taken arbitrarily small.In contrast to the previous approach, the trade-off parameter between separation and localization of the components is now .The size of the frequency support of the mother wavelet plays a fundamental role when dealing with multi-component signals; adjusting this parameter enables us to tune between good separation and good localization of the modes. In the sequel we will use the previous remark regarding the size of the frequency support of the mother wavelet to build a new RCM algorithm.
35A New RCMThe algorithm aims at identifying the ridge associated with each component from the magnitude of the WT and then reconstructing the components by using the information in the vicinity of the different ridges. It is based on three distinct steps:- first determine adaptively the number of modes- compute an optimal wavelet threshold- use threshold to retrieve the modes.Code at:
36Automatic detection of the number of modes Since, for a given time t, the scales of interest in the SST are those where is large enough, we aim at detecting the modes using the following sets:The natural expectation is that given t and when γ is appropriately chosen, each element will be associated with a single mode as defined above).Note: The normalization of the wavelet transform we use is necessary to give sense to the above set. We compute an estimate of the number of modes at time t is:The number of modes is defined as:
37Wavelet ThresholdOnce the number of modes is determined, we first consider the sets:and define T0 as the times t associated with non empty S0(t). A threshold for the WT and t in T0 is then computed as follows:
38The Algorithm for Retrieving the Modes We consider for each t in T0 the set Inf-k+1(t), which enables us to retrieve the mode k by applying the following approximation formula:Note that the index of I in the sum comes from the fact that Ik for small k corresponds to a high frequency component and that in Definition (2) the components are arranged in increasing frequency order.This reconstruction procedure is fully automatic and avoids the need to narrow down the curve searching in the time-scale space before proceeding with the mode retrieval.
39Example of a signal made of non-trivial intrinsic mode type signals
44Non Uniform SamplingTo adaptively choose non-uniform samples to minimize the interpolation error is a thorny issue. A method inspired by the EMD consists of selecting the extrema of the signal, such that they depend locally on the strength of the oscillations. Another possibility would be to choose equi-spaced sample points over the whole signal duration.Methods1. Consider non-uniform sample points for the location of the extrema of the high frequency mode given by the RCM proposed and then piecewise cubic Hermite interpolation of f at these points.2. Follow the same framework as the above, but the sample points being equi-spaced with the same average sampling rate.
48ReferencesE. Brevdo, N. S. Fuckar, G. Thakur, and H.-T. Wu, “The synchrosqueezing algorithm for time-varying spectral analysis: Robust properties and new plaeoclimate applications,” 2011, arXiv preprint ID:E. Chassande-Mottin, I. Daubechies, F. Auger, and P. Flandrin, “Differential reassignment,” IEEE Signal Process. Lett., vol. 4, no. 10, pp. 293–294, 1997.I. Daubechies, J. Lu, and H.-L. Wu, “Synchrosqueezed wavelet transforms: An empirical mode decomposition-like tool,” Appl. Computat. Harmon. Anal., vol. 20, no. 2, pp. 243–261, 2011.H.-T. Wu, P. Flandrin, and I. Daubechies, “One or two frequencies? The synchrosqueezing answer,” Adv. Adapt. Data Anal., vol. 3, no. 1–2, pp. 29–39, 2011.S. Mallat, A Wavelet Tour on Signal Processing. NewYork:Academic,1998.G. Rilling, P. Flandrin, and P. Goncalves, “On empirical mode decomposition and its algorithms,” in Proc. IEEE-EURASIP Workshop on Nonlin. Signal and Image Process. (NSIP), Grado (I), Jun, 2003.