Specify the path to the sample signals included with Signal Processing Toolbox™.

folder = fullfile(matlabroot,'toolbox','matlab','audiovideo');

Create a signal datastore that points to the specified folder and set sample rate variable name to Fs. List the names of the MAT-files in the datastore. Use the extractAfter function to extract the names from the full path.

sds = signalDatastore(folder,'FileExtension','.mat','SampleRateVariableName','Fs');
c = extractAfter(sds.Files,'audiovideo\')

c = 7x1 cell
    {0x0 char}
    {0x0 char}
    {0x0 char}
    {0x0 char}
    {0x0 char}
    {0x0 char}
    {0x0 char}

While the signal datastore has unread files, read consecutive files from the datastore. Use the progress function to monitor the fraction of files read.

while hasdata(sds)
    [data,info] = read(sds);
    fprintf('Fraction of files read: %.2f\n',progress(sds))
end

Fraction of files read: 0.14
Fraction of files read: 0.29
Fraction of files read: 0.43
Fraction of files read: 0.57
Fraction of files read: 0.71
Fraction of files read: 0.86
Fraction of files read: 1.00

Print and inspect the info structure returned by the last call to the read function.

info

info = struct with fields:
             SampleRate: 8192
       TimeVariableName: "Fs"
    SignalVariableNames: "y"
               FileName: "/mathworks/devel/bat/BR2020bd/build/matlab/toolbox/matlab/audiovideo/train.mat"

Create a signal datastore to iterate through the elements of an in-memory cell array of signal data. The data consists of a sinusoidally modulated linear chirp, a concave quadratic chirp, and a voltage controlled oscillator. The signals are sampled at 3000 Hz.

fs = 3000;
t = 0:1/fs:3-1/fs;
data = {chirp(t,300,t(end),800).*exp(2j*pi*10*cos(2*pi*2*t)); ...
        2*chirp(t,200,t(end),1000,'quadratic',[],'concave'); ...
        vco(sin(2*pi*t),[0.1 0.4]*fs,fs)};
sds = signalDatastore(data,'SampleRate',fs);

While the datastore has data, read each observation from the signal datastore and plot the short-time Fourier transform.

plotID = 1;
while hasdata(sds)
    [dataOut,info] = read(sds);
    subplot(3,1,plotID)
    stft(dataOut,info.SampleRate)
    plotID = plotID + 1;
end

Specify the path to four signals included with MATLAB®. The signals are recordings of a bird chirp, a gong, a train, and a splat. All signals are sampled at 8192 Hz.

folder = fullfile(matlabroot,'toolbox','matlab','audiovideo', ...
         ["chirp.mat","gong.mat","train.mat","splat.mat"]);

Create a signal datastore that points to the specified files. Each file contains the variable Fs that denotes the sample rate.

sds1 = signalDatastore(folder,'SampleRateVariableName','Fs');

Define a function that takes the output of the read function and calculates the upper and lower envelopes of the signals using spline interpolation over local maxima separated by at least 80 samples. The function also returns the sample times for each signal.

function [dataOut,infoOut] = signalEnvelope(dataIn,info)
    [dataOut(:,1),dataOut(:,2)] = envelope(dataIn,80,'peak');
    infoOut = info;
    infoOut.TimeInstants = (0:length(dataOut)-1)/info.SampleRate;
end

Call the transform function to create a second datastore, sds2, that computes the envelopes of the signals using the function you defined.

sds2 = transform(sds1,@signalEnvelope,"IncludeInfo",true);

Combine sds1 and sds2 create a third datastore. Each call to the read function from the combined datastore returns a matrix with three columns:

sdsCombined = combine(sds1,sds2);

Read and display the original data and the upper and lower envelopes from the combined datastore. Use the extractBetween function to extract the file name from the file path.

tiledlayout('flow')
while hasdata(sdsCombined)
    [dataOut,infoOut] = read(sdsCombined);
    ts = infoOut{2}.TimeInstants;
    nexttile
    hold on
    plot(ts,dataOut(:,1),'Color','#DCDCDC','LineStyle',':')
    plot(ts,dataOut(:,2:3),'Linewidth',1.5)
    hold off
    xlabel('Time (s)')
    ylabel('Signal')
    title(extractBetween(infoOut{:,2}.FileName,'audiovideo\','.mat'))
end

Specify the path to four files included with Signal Processing Toolbox™. Each file contains a chirp and a random sample rate, fs, ranging from 100 to 150 Hz. Create a signal datastore that points to the specified folder.

folder = fullfile(matlabroot,'examples','signal','data','sample_chirps', ...
    ["chirp_1.mat","chirp_4.mat","chirp_9.mat","chirp_10.mat"]);
sds = signalDatastore(folder,'SampleRateVariableName','fs');

Define a function that takes the output of the read function and computes and returns:

function [dataOut,infoOut] = extractSpectrogram(dataIn,info)
    [dataOut,F,T] = pspectrum(dataIn,info.SampleRate,'spectrogram',...
                              'TimeResolution',0.25,...
                              'OverlapPercent',40,'Leakage',0.8);
    infoOut = info;
    infoOut.CenterFrequencies = F;
    infoOut.TimeInstants = T;
end

Call the transform function to create a datastore that computes the spectrogram of each chirp using the function you defined.

sdsNew = transform(sds,@extractSpectrogram,'IncludeInfo',true);

While the transformed datastore has unread files, read from the new datastore and visualize the spectrograms in three-dimensional space.

t = tiledlayout('flow');
while hasdata(sdsNew)
    nexttile
    [sig,infoOut] = read(sdsNew);
    waterfall(infoOut.TimeInstants,infoOut.CenterFrequencies,sig)
    xlabel('Frequency (Hz)')
    ylabel('Time (S)')
    view([30 70])
end

Specify the file path to the example signals included with MATLAB®. Create a signal datastore that points to the specified folder.

folder = fullfile(matlabroot,'toolbox','matlab','audiovideo');
sds = signalDatastore(folder,'SampleRateVariableName','Fs');

Get the default number of partitions for the signal datastore.

n = numpartitions(sds)

n = 7

Partition the datastore into the default number of partitions and return the datastore corresponding to the fourth partition.

subsds = partition(sds,n,4);

Use the extractAfter function to display the name of the file contained in the datastore corresponding to the fourth partition.

fName = extractAfter(subsds.Files,'audiovideo\')

fName = 1x1 cell array
    {0x0 char}

Read the data and information about the signal in the datastore corresponding to the fourth partition. Extract the sample rate from info and resample the signal to half the original sample rate. Plot the original and resampled signals.

while hasdata(subsds)
    [data,info] = read(subsds);
    fs = info.SampleRate;
    f_res = 0.5*fs;
    ts = (0:length(data)-1)/fs;
    data_res = resample(data,1,2);
    t_res = (0:length(data_res)-1)/f_res;
    plot(ts,data,t_res,data_res,':')
    xlabel('Time (s)')
    ylabel('Signal')
    legend('Original','Resampled','Location','NorthWest')
end