Using Signal Analyzer App

App Workflow

A typical workflow for inspecting and comparing signals using the Signal Analyzer app is:

  1. Select Signals to Analyze — Select any signal available in the MATLAB® workspace. The app accepts numeric arrays and signals with inherent time information, such as MATLAB timetable arrays, timeseries objects, and labeledSignalSet objects. See Data Types Supported by Signal Analyzer for more information.

  2. Preprocess Signals — Lowpass, highpass, bandpass, or bandstop filter signals. Remove trends and compute signal envelopes. Smooth signals using moving averages, regression, Savitzky-Golay filters, or other methods. Change sample rates of signals or interpolate nonuniformly sampled signals onto uniform grids. Preprocess signals using your own custom functions. Generate MATLAB functions to automate preprocessing operations.

  3. Explore Signals — Add time information to signals using sample rates, numeric vectors, duration arrays, or MATLAB expressions. Plot, measure, and compare data, their spectra, their spectrograms, or their scalograms. Look for features and patterns in the time domain, in the frequency domain, and in the time-frequency domain. Compute persistence spectra to analyze sporadic signals and sharpen spectrogram estimates using reassignment. Extract regions of interest from signals.

  4. Share Analysis — Copy displays from the app to the clipboard as images. Export signals to the MATLAB workspace or save them to MAT-files. Generate MATLAB scripts to automate the computation of power spectrum, spectrogram, or persistence spectrum estimates and the extraction of regions of interest. Save Signal Analyzer sessions to resume your analysis later or on another machine.

 

Example: Extract Regions of Interest from Whale Song

Load a file that contains audio data from a Pacific blue whale, sampled at 4 kHz. The file is from the library of animal vocalizations maintained by the Cornell University Bioacoustics Research Program. The time scale in the data is compressed by a factor of 10 to raise the pitch and make the calls more audible. Convert the signal to a MATLAB® timetable.

whaleFile = fullfile(matlabroot,'examples','matlab','data','bluewhale.au');
[w,fs] = audioread(whaleFile);

whale = timetable(seconds((0:length(w)-1)'/fs),w);

% To hear, type soundsc(w,fs)

Open Signal Analyzer and drag the timetable to a display. Four features stand out from the noise. The first is known as a trill, and the other three are known as moans.

On the Display tab, click Spectrum to open a spectrum view and click Panner to activate the panner. Use the panner to create a zoom window with a width of about 2 seconds. Drag the zoom window so that it is centered on the trill. The spectrum shows a noticeable peak at around 900 Hz.

Isolate the single trill by highpass filtering. Right-click the signal in the Signal table and select Duplicate to create a copy of the whale song. Remove the original signal from the display by clearing the check box next to its name in the Signal table. On the Analyzer tab, click Preprocessing ? and select Highpass. Set the passband frequency to 925 Hz and the stopband attenuation at 80 dB. Use the default value for the steepness.

Clear the display and select the original signal. Extract the three moans to compare their spectra:

  1. Center the panner zoom window on the first moan. The spectrum has eight clearly defined peaks, located very close to multiples of 170 Hz. Click Extract Signals ? and select Between Time Limits.

  2. Click Panner to hide the panner. Press the space bar to see the full signal. Click Zoom in X and zoom in on a 2-second interval of the time view centered on the second moan. The spectrum again has peaks at multiples of 170 Hz. Click Extract Signals ? and select Between Time Limits.

  3. Press the space bar to see the full signal. Click Data Cursors ? and select Two. Place the time domain cursors in a 2-second interval around the third moan. Again, there are peaks at multiples of 170 Hz. Click Extract Signals ? and select Between Time Cursors.

Plot the highpass-filtered signal and place the two data cursors at 1 second and 3.5 seconds. Extract the region containing the trill.

Remove the original signal from the display by clearing the check box next to its name in the Signal table. Display the three regions of interest you just extracted. Their spectra lie approximately on top of each other.

On the same display, plot the region of interest containing the trill that you extracted. The trill and moan spectra are noticeably different.

Click on Export on the Analyzer tab to export the four regions of interest in a MAT-file.

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