Find and Track Ridges Using Reassigned Spectrogram

Load a datafile containing an echolocation pulse emitted by a big brown bat (Eptesicus fuscus) and measured with a sampling interval of 7 microseconds. Create a MATLAB® timetable using the signal and the time information.

load batsignal

t = (0:length(batsignal)-1)*DT;
sg = timetable(seconds(t)',batsignal);

Open Signal Analyzer and drag the timetable from the Workspace browser to the Signal table. Click Display Grid ? to create two side-by-side displays. Select each display and click the Time-Frequency button to add a spectrogram view.

Drag the timetable to both displays.

Select the Spectrogram tab. On the display at right, check Reassign. For each display:

  • Set the time resolution to 280 microseconds and specify 85% overlap between adjoining segments.

  • Use the Leakage slider to increase the leakage until the RBW is about 4.5 kHz.

  • Set the power limits to –45 dB and –20 dB.

The reassigned spectrogram clearly shows three time-frequency ridges. To track the ridges, select the display at right. On the Display tab, click Generate Script and select Spectrogram Script. The script appears in the Editor.

% Compute spectrogram

% Generated by MATLAB(R) 9.7 and Signal Processing Toolbox 8.2.
% Generated on: 26-Dec-2018 17:21:44

% Parameters
timeLimits = seconds([0 0.002793]); % seconds
frequencyLimits = [0 71428.57]; % Hz
leakage = 0.9;
timeResolution = 0.00028; % seconds
overlapPercent = 85;
reassignFlag = true;

%%
% Index into signal time region of interest
sg_batsignal_ROI = sg(:,'batsignal');
sg_batsignal_ROI = sg_batsignal_ROI(timerange(timeLimits(1),timeLimits(2),'closed'),1);

% Compute spectral estimate
% Run the function call below without output arguments to plot the results
[P,F,T] = pspectrum(sg_batsignal_ROI, ...
    'spectrogram', ...
    'FrequencyLimits',frequencyLimits, ...
    'Leakage',leakage, ...
    'TimeResolution',timeResolution, ...
    'OverlapPercent',overlapPercent, ...
    'Reassign',reassignFlag);

Run the script. Plot the reassigned spectrogram.

mesh(seconds(T),F,P)
xlabel('Time')
ylabel('Frequency')
axis tight
view(2)
colormap pink

Use the tfridge function to track the ridges.

[fridge,~,lridge] = tfridge(P,F,0.01,'NumRidges',3,'NumFrequencyBins',10);

hold on
plot3(seconds(T),fridge,P(lridge),':','linewidth',3)
hold off

Thanks to Curtis Condon, Ken White, and Al Feng of the Beckman Center at the University of Illinois for the bat data and permission to use it in this example.

Matlabsolutions.com provides guaranteed satisfaction with a commitment to complete the work within time. Combined with our meticulous work ethics and extensive domain experience, We are the ideal partner for all your homework/assignment needs. We pledge to provide 24*7 support to dissolve all your academic doubts. We are composed of 300+ esteemed Matlab and other experts who have been empanelled after extensive research and quality check.

Matlabsolutions.com provides undivided attention to each Matlab assignment order with a methodical approach to solution. Our network span is not restricted to US, UK and Australia rather extends to countries like Singapore, Canada and UAE. Our Matlab assignment help services include Image Processing Assignments, Electrical Engineering Assignments, Matlab homework help, Matlab Research Paper help, Matlab Simulink help. Get your work done at the best price in industry.

Machine Learning in MATLAB

Train Classification Models in Classification Learner App

Train Regression Models in Regression Learner App

Distribution Plots

Explore the Random Number Generation UI

Design of Experiments

Machine Learning Models

Logistic regression

Logistic regression create generalized linear regression model - MATLAB fitglm 2

Support Vector Machines for Binary Classification

Support Vector Machines for Binary Classification 2

Support Vector Machines for Binary Classification 3

Support Vector Machines for Binary Classification 4

Support Vector Machines for Binary Classification 5

Assess Neural Network Classifier Performance

Naive Bayes Classification

ClassificationTree class

Discriminant Analysis Classification

Ensemble classifier

ClassificationTree class 2

Train Generalized Additive Model for Binary Classification

Train Generalized Additive Model for Binary Classification 2

Classification Using Nearest Neighbors

Classification Using Nearest Neighbors 2

Classification Using Nearest Neighbors 3

Classification Using Nearest Neighbors 4

Classification Using Nearest Neighbors 5

Linear Regression

Linear Regression 2

Linear Regression 3

Linear Regression 4

Nonlinear Regression

Nonlinear Regression 2

Visualizing Multivariate Data

Generalized Linear Models

Generalized Linear Models 2

RegressionTree class

RegressionTree class 2

Neural networks

Gaussian Process Regression Models

Gaussian Process Regression Models 2

Understanding Support Vector Machine Regression

Understanding Support Vector Machine Regression 2

RegressionEnsemble



matlab assignment help


matlab assignment help