augment

Augment audio data

Description

example

data = augment(aug,audioIn) returns a table containing augmented audio data and information about the augmentation applied.

example

data = augment(aug,audioIn,fs) specifies the sample rate of the audio input.

Examples

collapse all

Read in an audio signal and listen to it.

[audioIn,fs] = audioread("Counting-16-44p1-mono-15secs.wav");
sound(audioIn,fs)

Create an audioDataAugmenter object that applies time stretching, volume control, and time shifting in cascade. Apply each of the augmentations with 80% probability. Set NumAugmentations to 5 to output five independently augmented signals. To skip pitch shifting and noise addition for each augmentation, set the respective probabilities to 0. Define parameter ranges for each relevant augmentation algorithm.

augmenter = audioDataAugmenter( ...
    "AugmentationMode","sequential", ...
    "NumAugmentations",5, ...
    ...
    "TimeStretchProbability",0.8, ...
    "SpeedupFactorRange", [1.3,1.4], ...
    ...
    "PitchShiftProbability",0, ...
    ...
    "VolumeControlProbability",0.8, ...
    "VolumeGainRange",[-5,5], ...
    ...
    "AddNoiseProbability",0, ...
    ...
    "TimeShiftProbability",0.8, ...
    "TimeShiftRange", [-500e-3,500e-3])
augmenter = 
  audioDataAugmenter with properties:

               AugmentationMode: "sequential"
    AugmentationParameterSource: 'random'
               NumAugmentations: 5
         TimeStretchProbability: 0.8000
             SpeedupFactorRange: [1.3000 1.4000]
          PitchShiftProbability: 0
       VolumeControlProbability: 0.8000
                VolumeGainRange: [-5 5]
            AddNoiseProbability: 0
           TimeShiftProbability: 0.8000
                 TimeShiftRange: [-0.5000 0.5000]

Call augment on the audio to create 5 augmentations. The augmented audio is returned in a table with variables Audio and AugmentationInfo. The number of rows in the table is defined by NumAugmentations.

data = augment(augmenter,audioIn,fs)
data=5×2 table
          Audio          AugmentationInfo
    _________________    ________________

    {685056x1 double}      [1x1 struct]  
    {685056x1 double}      [1x1 struct]  
    {505183x1 double}      [1x1 struct]  
    {685056x1 double}      [1x1 struct]  
    {490728x1 double}      [1x1 struct]  

In the current augmentation pipeline, augmentation parameters are assigned randomly from within the specified ranges. To determine the exact parameters used for an augmentation, inspect AugmentationInfo.

augmentationToInspect = 4;
data.AugmentationInfo(augmentationToInspect)
ans = struct with fields:
    SpeedupFactor: 1
       VolumeGain: 4.3399
        TimeShift: 0.4502

Listen to the augmentation you are inspecting. Plot time representation of the original and augmented signals.

augmentation = data.Audio{augmentationToInspect};
sound(augmentation,fs)

t = (0:(numel(audioIn)-1))/fs;
taug = (0:(numel(augmentation)-1))/fs;
plot(t,audioIn,taug,augmentation)
legend("Original Audio","Augmented Audio")
ylabel("Amplitude")
xlabel("Time (s)")

Read in an audio signal and listen to it.

[audioIn,fs] = audioread("Counting-16-44p1-mono-15secs.wav");
sound(audioIn,fs)

Create an audioDataAugmenter object that applies time stretching, pitch shifting, and noise corruption in cascade. Specify the time stretch speedup factors as 0.9, 1.1, and 1.2. Specify the pitch shifting in semitones as -2, -1, 1, and 2. Specify the noise corruption SNR as 10 dB and 15 dB.

augmenter = audioDataAugmenter( ...
    "AugmentationMode","sequential", ...
    "AugmentationParameterSource","specify", ...
    "SpeedupFactor",[0.9,1.1,1.2], ...
    "ApplyTimeStretch",true, ...
    "ApplyPitchShift",true, ...
    "SemitoneShift",[-2,-1,1,2], ...
    "SNR",[10,15], ...
    "ApplyVolumeControl",false, ...
    "ApplyTimeShift",false)
augmenter = 
  audioDataAugmenter with properties:

               AugmentationMode: "sequential"
    AugmentationParameterSource: "specify"
               ApplyTimeStretch: 1
                  SpeedupFactor: [0.9000 1.1000 1.2000]
                ApplyPitchShift: 1
                  SemitoneShift: [-2 -1 1 2]
             ApplyVolumeControl: 0
                  ApplyAddNoise: 1
                            SNR: [10 15]
                 ApplyTimeShift: 0

Call augment on the audio to create 24 augmentations. The augmentations represent every combination of the specified augmentation parameters (3×4×2=24).

data = augment(augmenter,audioIn,fs)
data=24×2 table
          Audio          AugmentationInfo
    _________________    ________________

    {761243x1 double}      [1x1 struct]  
    {622888x1 double}      [1x1 struct]  
    {571263x1 double}      [1x1 struct]  
    {761243x1 double}      [1x1 struct]  
    {622888x1 double}      [1x1 struct]  
    {571263x1 double}      [1x1 struct]  
    {761243x1 double}      [1x1 struct]  
    {622888x1 double}      [1x1 struct]  
    {571263x1 double}      [1x1 struct]  
    {761243x1 double}      [1x1 struct]  
    {622888x1 double}      [1x1 struct]  
    {571263x1 double}      [1x1 struct]  
    {761243x1 double}      [1x1 struct]  
    {622888x1 double}      [1x1 struct]  
    {571263x1 double}      [1x1 struct]  
    {761243x1 double}      [1x1 struct]  
      ⋮

You can check the parameter configuration of each augmentation using the AugmentationInfo table variable.

augmentationToInspect = 1;
data.AugmentationInfo(augmentationToInspect)
ans = struct with fields:
    SpeedupFactor: 0.9000
    SemitoneShift: -2
              SNR: 10

Listen to the augmentation you are inspecting. Plot the time-domain representation of the original and augmented signals.

augmentation = data.Audio{augmentationToInspect};
sound(augmentation,fs)

t = (0:(numel(audioIn)-1))/fs;
taug = (0:(numel(augmentation)-1))/fs;
plot(t,audioIn,taug,augmentation)
legend("Original Audio","Augmented Audio")
ylabel("Amplitude")
xlabel("Time (s)")

Read in an audio signal and listen to it.

[audioIn,fs] = audioread("Counting-16-44p1-mono-15secs.wav");

Create an audioDataAugmenter object that applies noise corruption, and time shifting in parallel branches. For the noise corruption branch, randomly apply noise with an SNR in the range 0 dB to 20 dB. For the time shifting branch, randomly apply time shifting in the range -300 ms to 300 ms. Apply augmentation 2 times for each branch, for 4 total augmentations.

augmenter = audioDataAugmenter( ...
    "AugmentationMode","independent", ...
    "AugmentationParameterSource","random", ...
    "NumAugmentations",2, ...
    "ApplyTimeStretch",false, ...
    "ApplyPitchShift",false, ...
    "ApplyVolumeControl",false, ...
    "SNRRange",[0,20], ...
    "TimeShiftRange",[-300e-3,300e-3])
augmenter = 
  audioDataAugmenter with properties:

               AugmentationMode: "independent"
    AugmentationParameterSource: "random"
               NumAugmentations: 2
               ApplyTimeStretch: 0
                ApplyPitchShift: 0
             ApplyVolumeControl: 0
                  ApplyAddNoise: 1
                       SNRRange: [0 20]
                 ApplyTimeShift: 1
                 TimeShiftRange: [-0.3000 0.3000]

Call augment on the audio to create 3 augmentations.

data = augment(augmenter,audioIn,fs);
  

You can check the parameter configuration of each augmentation using the AugmentatioInfo table variable.

augmentationToInspect = 4;
data.AugmentationInfo{augmentationToInspect}
ans = struct with fields:
    TimeShift: 0.0016

Listen to the audio you are inspecting. Plot the time-domain representation of the original and augmented signals.

augmentation = data.Audio{augmentationToInspect};
sound(augmentation,fs)

t = (0:(numel(audioIn)-1))/fs;
taug = (0:(numel(augmentation)-1))/fs;
plot(t,audioIn,taug,augmentation)
legend("Original Audio","Augmented Audio")
ylabel("Amplitude")
xlabel("Time (s)")

Read in an audio signal and listen to it.

[audioIn,fs] = audioread("Counting-16-44p1-mono-15secs.wav");

Create an audioDataAugmenter object that applies volume control, noise corruption, and time shifting in parallel branches.

augmenter = audioDataAugmenter( ...
    "AugmentationMode","independent", ...
    "AugmentationParameterSource","specify", ...
    "ApplyTimeStretch",false, ...
    "ApplyPitchShift",false, ...
    "VolumeGain",2, ...
    "SNR",0, ...
    "TimeShift",2)
augmenter = 
  audioDataAugmenter with properties:

               AugmentationMode: "independent"
    AugmentationParameterSource: "specify"
               ApplyTimeStretch: 0
                ApplyPitchShift: 0
             ApplyVolumeControl: 1
                     VolumeGain: 2
                  ApplyAddNoise: 1
                            SNR: 0
                 ApplyTimeShift: 1
                      TimeShift: 2

Call augment on the audio to create 3 augmentations.

data = augment(augmenter,audioIn,fs)
data=3×2 table
          Audio          AugmentationInfo
    _________________    ________________

    {685056x1 double}      {1x1 struct}  
    {685056x1 double}      {1x1 struct}  
    {685056x1 double}      {1x1 struct}  

You can check the parameter configuration of each augmentation using the AugmentatioInfo table variable.

augmentationToInspect = 3;
data.AugmentationInfo{augmentationToInspect}
ans = struct with fields:
    TimeShift: 2

Listen to the audio you are inspecting. Plot the time-domain representations of the original and augmented signals.

augmentation = data.Audio{augmentationToInspect};
sound(augmentation,fs)

t = (0:(numel(audioIn)-1))/fs;
taug = (0:(numel(augmentation)-1))/fs;
plot(t,audioIn,taug,augmentation)
legend("Original Audio","Augmented Audio")
ylabel("Amplitude")
xlabel("Time (s)")

The audioDataAugmenter supports multiple workflows for augmenting your datastore, including:

  • Offline augmentation

  • Augmentation using tall arrays

  • Augmentation using transform datastores

In each workflow, begin by creating an audio datastore to point to your audio data. In this example, you create an audio datastore that points to audio samples included with Audio Toolbox™. Count the number of files in the dataset.

folder = fullfile(matlabroot,"toolbox","audio","samples");
ADS = audioDatastore(folder)
ADS = 
  audioDatastore with properties:

                       Files: {
                              ' ...\matlab\toolbox\audio\samples\Ambiance-16-44p1-mono-12secs.wav';
                              ' ...\matlab\toolbox\audio\samples\AudioArray-16-16-4channels-20secs.wav';
                              ' ...\toolbox\audio\samples\ChurchImpulseResponse-16-44p1-mono-5secs.wav'
                               ... and 26 more
                              }
    AlternateFileSystemRoots: {}
              OutputDataType: 'double'
                      Labels: {}

numFilesInDataset = numel(ADS.Files)
numFilesInDataset = 29

Create an audioDataAugmenter that applies random sequential augmentations. Set NumAugmentations to 2.

aug = audioDataAugmenter('NumAugmentations',2)
aug = 
  audioDataAugmenter with properties:

               AugmentationMode: 'sequential'
    AugmentationParameterSource: 'random'
               NumAugmentations: 2
         TimeStretchProbability: 0.5000
             SpeedupFactorRange: [0.8000 1.2000]
          PitchShiftProbability: 0.5000
             SemitoneShiftRange: [-2 2]
       VolumeControlProbability: 0.5000
                VolumeGainRange: [-3 3]
            AddNoiseProbability: 0.5000
                       SNRRange: [0 10]
           TimeShiftProbability: 0.5000
                 TimeShiftRange: [-0.0050 0.0050]

Offline Augmentation

To augment the audio dataset, create two augmentations of each file and then write the augmentations as WAV files.

while hasdata(ADS)
    [audioIn,info] = read(ADS);
    
    data = augment(aug,audioIn,info.SampleRate);
    
    [~,fn] = fileparts(info.FileName);
    for i = 1:size(data,1)
        augmentedAudio = data.Audio{i};
        
        % If augmentation caused an audio signal to have values outside of -1 and 1, 
        % normalize the audio signal to avoid clipping when writing.
        if max(abs(augmentedAudio),[],'all')>1
            augmentedAudio = augmentedAudio/max(abs(augmentedAudio),[],'all');
        end
        
        audiowrite(sprintf('%s_aug%d.wav',fn,i),augmentedAudio,info.SampleRate)
    end
end

Create an audioDatastore that points to the augmented dataset and confirm that the number of files in the dataset is double the original number of files.

augmentedADS = audioDatastore(pwd)
augmentedADS = 
  audioDatastore with properties:

                       Files: {
                              ' ...\Examples\audio-ex28074079\Ambiance-16-44p1-mono-12secs_aug1.wav';
                              ' ...\Examples\audio-ex28074079\Ambiance-16-44p1-mono-12secs_aug2.wav';
                              ' ...\Examples\audio-ex28074079\AudioArray-16-16-4channels-20secs_aug1.wav'
                               ... and 55 more
                              }
    AlternateFileSystemRoots: {}
              OutputDataType: 'double'
                      Labels: {}

numFilesInAugmentedDataset = numel(augmentedADS.Files)
numFilesInAugmentedDataset = 58

Augment Using Tall Arrays

When augmenting a dataset using tall arrays, the input data to the augmenter should be sampled at a consistent rate. Subset the original audio dataset to only include files with a sample rate of 44.1 kHz. Most datasets are already cleaned to have a consistent sample rate.

keepFile = cellfun(@(x)contains(x,'44p1'),ADS.Files);
ads44p1 = subset(ADS,keepFile);
fs = 44.1e3;

Convert the audio datastore to a tall array. tall arrays are evaluated only when you request them explicitly using gather. MATLAB® automatically optimizes the queued calculations by minimizing the number of passes through the data. If you have the Parallel Computing Toolbox™, you can spread the calculations across multiple machines. The audio data is represented as an M-by-1 tall cell array, where M is the number of files in the audio datastore.

adsTall = tall(ads44p1)
Starting parallel pool (parpool) using the 'local' profile ...
Connected to the parallel pool (number of workers: 6).

adsTall =

  M×1 tall cell array

    { 539648×1 double}
    { 227497×1 double}
    {   8000×1 double}
    { 685056×1 double}
    { 882688×2 double}
    {1115760×2 double}
    { 505200×2 double}
    {3195904×2 double}
        :         :
        :         :

Define a cellfun function so that augmentation is applied to each cell of the tall array. Call gather to evaluate the tall array.

augTall = cellfun(@(x)augment(aug,x,fs),adsTall,"UniformOutput",false);
augmentedDataset = gather(augTall)
Evaluating tall expression using the Parallel Pool 'local':
- Pass 1 of 1: Completed in 1 min 34 sec
Evaluation completed in 1 min 34 sec
augmentedDataset=12×1 cell
    {2×2 table}
    {2×2 table}
    {2×2 table}
    {2×2 table}
    {2×2 table}
    {2×2 table}
    {2×2 table}
    {2×2 table}
    {2×2 table}
    {2×2 table}
    {2×2 table}
    {2×2 table}

The augmented dataset is returned as a numFiles-by-1 cell array, where numFiles is the number of files in the datastore. Each element of the cell array is a numAugmentationsPerFile-by-2 table, where numAugmentationsPerFile is the number of augmentations returned per file.

numFiles = numel(augmentedDataset)
numFiles = 12
numAugmentationsPerFile = size(augmentedDataset{1},1)
numAugmentationsPerFile = 2

Augment Using Transform Datastore

You can perform online data augmentation while you train your machine learning application using a transform datastore. Call transform to create a new datastore that applies data augmentation while reading.

transformADS = transform(ADS,@(x,info)augment(aug,x,info),'IncludeInfo',true)
transformADS = 
  TransformedDatastore with properties:

    UnderlyingDatastore: [1×1 audioDatastore]
             Transforms: {@(x,info)augment(aug,x,info)}
            IncludeInfo: 1

Call read to return the augmented first file from the transform datastore.

augmentedRead = read(transformADS)
augmentedRead=2×2 table
          Audio          AugmentationInfo
    _________________    ________________

    {539648×1 double}      [1×1 struct]  
    {586683×1 double}      [1×1 struct]  

Input Arguments

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Audio input, specified as a column vector or matrix of independent channels (columns).

Data Types: single | double

Sample rate in Hz, specified as a positive scalar. The allowable range of fs depends on the properties of the audioDataAugmenter object.

Data Types: single | double

Output Arguments

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Augmented audio and augmentation information, returned as a two-column table. The first column holds the augmented audio signal. The second column holds information about the applied augmentation methods. The number of rows in data corresponds to the number of output augmented signals. The number of output augmented signals depends on the property values of the object.

Introduced in R2019b