transform
Transform audio datastore
Description
creates a new datastore that transforms output from the transformDatastore
= transform(ADS
,@fcn
)read
function.
specifies options using one or more transformDatastore
= transform(ADS
,@fcn
,Name,Value
)Name,Value
pair arguments.
Examples
Output Mono Audio from Datastore
Specify the file path to the audio samples included with Audio Toolbox™. Create an audio datastore that points to the specified folder.
folder = fullfile(matlabroot,'toolbox','audio','samples'); ADS = audioDatastore(folder);
Call transform
to create a new datastore that mixes multichannel signals to mono.
ADSnew = transform(ADS,@(x)mean(x,2));
Read from the new datastore and confirm that it only outputs mono signals.
while hasdata(ADSnew) audio = read(ADSnew); fprintf('Number of channels = %d\n',size(audio,2)) end
Number of channels = 1 Number of channels = 1 Number of channels = 1 Number of channels = 1 Number of channels = 1 Number of channels = 1 Number of channels = 1 Number of channels = 1 Number of channels = 1 Number of channels = 1 Number of channels = 1 Number of channels = 1 Number of channels = 1 Number of channels = 1 Number of channels = 1 Number of channels = 1 Number of channels = 1 Number of channels = 1 Number of channels = 1 Number of channels = 1 Number of channels = 1 Number of channels = 1 Number of channels = 1 Number of channels = 1 Number of channels = 1 Number of channels = 1 Number of channels = 1 Number of channels = 1 Number of channels = 1 Number of channels = 1 Number of channels = 1 Number of channels = 1 Number of channels = 1 Number of channels = 1 Number of channels = 1 Number of channels = 1 Number of channels = 1 Number of channels = 1 Number of channels = 1
Clip Audio to Five Seconds
The audio samples included with Audio Toolbox™ have varying durations. Use the transform
function to customize the read
function so that it outputs a random five second segment of the audio samples.
Specify the file path to the audio samples included with Audio Toolbox. Create an audio datastore that points to the specified folder.
folder = fullfile(matlabroot,'toolbox','audio','samples'); ADS = audioDatastore(folder);
Define a function to take as input the output of the read
function. Make the function extract five seconds worth of data from the audio signal.
function [dataOut,info] = extractSegment(audioIn,info) [N,numChan] = size(audioIn); newN = round(info.SampleRate*5); if newN > N % signal length < 5 seconds numPad = newN - N + 1; dataOut = [audioIn;zeros(numPad,numChan,'like',audioIn)]; elseif newN < N % signal length > 5 seconds start = randi(N - newN + 1); dataOut = audioIn(start:start+newN-1,:); else % signal length == 5 seconds dataOut = audioIn; end end
Call transform
to create a TransformedDatastore
with Transforms
set to the function you defined.
ADSnew = transform(ADS,@extractSegment,'IncludeInfo',true)
ADSnew = TransformedDatastore with properties: UnderlyingDatastores: {audioDatastore} SupportedOutputFormats: ["txt" "csv" "dat" "asc" "xlsx" "xls" "parquet" "parq" "png" "jpg" "jpeg" "tif" "tiff" "wav" "flac" "ogg" "opus" "mp3" "mp4" "m4a"] Transforms: {[@extractSegment]} IncludeInfo: 1
Read the first three audio files and verify that the outputs are five second segments.
for i = 1:3 [audio,info] = read(ADSnew); fprintf('Duration = %d seconds\n',size(audio,1)/info.SampleRate) end
Duration = 5 seconds Duration = 5 seconds Duration = 5 seconds
Output Mel Spectrogram
Use transform
to create an audio datastore that returns a mel spectrogram representation from the read
function.
Specify the file path to the audio samples included with Audio Toolbox™. Create an audio datastore that points to the specified folder.
folder = fullfile(matlabroot,'toolbox','audio','samples'); ADS = audioDatastore(folder);
Define a function that transforms audio data from a time-domain representation to a log mel spectrogram. The function adds the additional outputs from the melSpectrogram
function to the info struct output from reading the audio datastore.
function [dataOut,infoOut] = extractMelSpectrogram(audioIn,info) [S,F,T] = melSpectrogram(audioIn,info.SampleRate); dataOut = 10*log10(S+eps); infoOut = info; infoOut.CenterFrequencies = F; infoOut.TimeInstants = T; end
Call transform
to create a TransformedDatastore
with Transforms
set to extractMelSpectrogram
.
ADSnew = transform(ADS,@extractMelSpectrogram,'IncludeInfo',true)
ADSnew = TransformedDatastore with properties: UnderlyingDatastores: {audioDatastore} SupportedOutputFormats: ["txt" "csv" "dat" "asc" "xlsx" "xls" "parquet" "parq" "png" "jpg" "jpeg" "tif" "tiff" "wav" "flac" "ogg" "opus" "mp3" "mp4" "m4a"] Transforms: {[@extractMelSpectrogram]} IncludeInfo: 1
Read the first three audio files and plot the log mel spectrograms. If there are multiple channels, plot only the first channel.
for i = 1:3 [melSpec,info] = read(ADSnew); figure(i) surf(info.TimeInstants,info.CenterFrequencies,melSpec(:,:,1),'EdgeColor','none'); xlabel('Time (s)') ylabel('Frequency (Hz)') [~,name] = fileparts(info.FileName); title(name) axis([0 info.TimeInstants(end) info.CenterFrequencies(1) info.CenterFrequencies(end)]) view([0,90]) end
Output Spectral Shape Features
Use transform
to create an audio datastore that returns feature vectors.
Specify the file path to the audio samples included with Audio Toolbox™. Create an audio datastore that points to the specified folder.
folder = fullfile(matlabroot,'toolbox','audio','samples'); ADS = audioDatastore(folder);
Define a function, extractFeatureVector
, that transforms the audio data from a time-domain representation to feature vectors.
function [dataOut,info] = extractFeatureVector(audioIn,info) % Convert to frequency-domain representation windowLength = 256; overlapLength = 128; [~,f,~,S] = spectrogram(mean(audioIn,2), ... hann(windowLength,"Periodic"), ... overlapLength, ... windowLength, ... info.SampleRate, ... "power", ... "onesided"); % Extract features [kurtosis,spread,centroid] = spectralKurtosis(S,f); skewness = spectralSkewness(S,f); crest = spectralCrest(S,f); decrease = spectralDecrease(S,f); entropy = spectralEntropy(S,f); flatness = spectralFlatness(S,f); flux = spectralFlux(S,f); rolloff = spectralRolloffPoint(S,f); slope = spectralSlope(S,f); % Concatenate to create feature vectors dataOut = [kurtosis,spread,centroid,skewness,crest,decrease,entropy,flatness,flux,rolloff,slope]; end
Call transform
to create a TransformedDatastore
with Transforms
set to extractFeatureVector
.
ADSnew = transform(ADS,@extractFeatureVector,'IncludeInfo',true)
ADSnew = TransformedDatastore with properties: UnderlyingDatastores: {audioDatastore} SupportedOutputFormats: ["txt" "csv" "dat" ... ] (1x20 string) Transforms: {[@extractFeatureVector]} IncludeInfo: 1
Call read
to return the feature vectors for the audio over time.
featureMatrix = read(ADSnew); [numFeatureVectors,numFeatures] = size(featureMatrix)
numFeatureVectors = 4215 numFeatures = 11
Apply Bandpass Filtering
Use transform
to create an audio datastore that applies bandpass filtering before returning audio from the read
function.
Specify the file path to the audio samples included with Audio Toolbox™. Create an audio datastore that points to the specified folder.
folder = fullfile(matlabroot,'toolbox','audio','samples'); ADS = audioDatastore(folder);
Define a function, applyBandpassFilter
, that applies a bandpass filter with a passband between 1 and 15 kHz.
function [audioOut,info] = applyBandpassFilter(audioIn,info) audioOut = bandpass(audioIn,[1e3,15e3],info.SampleRate); end
Call transform
to create a TransformedDatastore
with Transforms
set to applyBandpassFilter
.
ADSnew = transform(ADS,@applyBandpassFilter,'IncludeInfo',true)
ADSnew = TransformedDatastore with properties: UnderlyingDatastores: {audioDatastore} SupportedOutputFormats: ["txt" "csv" "dat" "asc" "xlsx" "xls" "parquet" "parq" "png" "jpg" "jpeg" "tif" "tiff" "wav" "flac" "ogg" "opus" "mp3" "mp4" "m4a"] Transforms: {[@applyBandpassFilter]} IncludeInfo: 1
Call read
to return the bandpass filtered audio from the transform datastore. Call read
to return the bandpass filtered audio from the original datastore. Plot the spectrograms to visualize the difference.
[audio1,info1] = read(ADS); [audio2,info2] = read(ADSnew);
spectrogram(audio1,hann(512),256,512,info1.SampleRate,'yaxis') title('Original Signal')
spectrogram(audio2,hann(512),256,512,info2.SampleRate,'yaxis') title('Filtered Signal')
Input Arguments
ADS
— Audio datastore
audioDatastore
object
Audio datastore, specified as an audioDatastore
object.
@fcn
— Function that transforms data
function handle
Function that transforms data, specified as a function handle. The signature of the
function depends on the IncludeInfo
parameter.
If
IncludeInfo
is set tofalse
(default), the function transforms the audio output fromread
. The info output fromread
is unaltered.The transform function must have this signature:
function dataOut = fcn(audio) ... end
If
IncludeInfo
is set totrue
, the function transforms the audio output fromread
, and can use or modify the information returned fromread
.The transform function must have this signature:
function [dataOut,infoOut] = fcn(audio,infoIn) ... end
Name-Value Arguments
Specify optional pairs of arguments as
Name1=Value1,...,NameN=ValueN
, where Name
is
the argument name and Value
is the corresponding value.
Name-value arguments must appear after other arguments, but the order of the
pairs does not matter.
Before R2021a, use commas to separate each name and value, and enclose
Name
in quotes.
Example: 'IncludeInfo',tf
IncludeInfo
— Pass info through customized read
function
false
(default) | true
Pass info through the customized read function, specified as
true
or false
. If true
, the
transform function can use or modify the information it gets from
read
. If unspecified, IncludeInfo
defaults
to false
.
Data Types: logical
Output Arguments
transformDatastore
— New datastore with customized read
TransformedDatastore
New datastore with customized read
,
returned as a TransformedDatastore
with
UnderlyingDatastore
set to ADS
,
Transforms
set to fcn
, and
IncludeInfo
set to true
or
false
.
Version History
Introduced in R2019a
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