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crepe

CREPE neural network

Since R2021a

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    Syntax

    net = crepe
    net = crepe('ModelCapacity',CAP)

    Description

    example

    net = crepe returns a pretrained CREPE model.

    This function requires both Audio Toolbox™ and Deep Learning Toolbox™.

    net = crepe('ModelCapacity',CAP) specifies the model capacity.

    For example, net = crepe('ModelCapacity','small') specifies the model capacity as small.

    Examples

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    This example uses:

    Open Live Script

    Download and unzip the Audio Toolbox™ model for CREPE.

    Type crepe at the Command Window. If the Audio Toolbox model for CREPE is not installed, then the function provides a link to the location of the network weights. To download the model, click the link and unzip the file to a location on the MATLAB path.

    Alternatively, execute these commands to download and unzip the CREPE model to your temporary directory.

    downloadFolder = fullfile(tempdir,'crepeDownload');
loc = websave(downloadFolder,'https://ssd.bat365/supportfiles/audio/crepe.zip');
crepeLocation = tempdir;
unzip(loc,crepeLocation)
addpath(fullfile(crepeLocation,'crepe'))

    Check that the installation is successful by typing crepe at the Command Window. If the network is installed, then the function returns a DAGNetwork (Deep Learning Toolbox) object.

    crepe
    ans = 
  DAGNetwork with properties:

         Layers: [34×1 nnet.cnn.layer.Layer]
    Connections: [33×2 table]
     InputNames: {'input'}
    OutputNames: {'pitch'}

    This example uses:

    Open Live Script

    Load a pretrained CREPE convolutional neural network and examine the layers and classes.

    Use crepe to load the pretrained CREPE network. The output net is a DAGNetwork (Deep Learning Toolbox) object.

    net = crepe
    net = 
  DAGNetwork with properties:

         Layers: [34×1 nnet.cnn.layer.Layer]
    Connections: [33×2 table]
     InputNames: {'input'}
    OutputNames: {'pitch'}

    View the network architecture using the Layers property. The network has 34 layers. There are 13 layers with learnable weights, of which six are convolutional layers, six are batch normalization layers, and one is a fully connected layer.

    net.Layers
    ans = 
  34×1 Layer array with layers:

     1   'input'                Image Input           1024×1×1 images
     2   'conv1'                Convolution           1024 512×1×1 convolutions with stride [4  1] and padding 'same'
     3   'conv1_relu'           ReLU                  ReLU
     4   'conv1-BN'             Batch Normalization   Batch normalization with 1024 channels
     5   'conv1-maxpool'        Max Pooling           2×1 max pooling with stride [2  1] and padding [0  0  0  0]
     6   'conv1-dropout'        Dropout               25% dropout
     7   'conv2'                Convolution           128 64×1×1024 convolutions with stride [1  1] and padding 'same'
     8   'conv2_relu'           ReLU                  ReLU
     9   'conv2-BN'             Batch Normalization   Batch normalization with 128 channels
    10   'conv2-maxpool'        Max Pooling           2×1 max pooling with stride [2  1] and padding [0  0  0  0]
    11   'conv2-dropout'        Dropout               25% dropout
    12   'conv3'                Convolution           128 64×1×128 convolutions with stride [1  1] and padding 'same'
    13   'conv3_relu'           ReLU                  ReLU
    14   'conv3-BN'             Batch Normalization   Batch normalization with 128 channels
    15   'conv3-maxpool'        Max Pooling           2×1 max pooling with stride [2  1] and padding [0  0  0  0]
    16   'conv3-dropout'        Dropout               25% dropout
    17   'conv4'                Convolution           128 64×1×128 convolutions with stride [1  1] and padding 'same'
    18   'conv4_relu'           ReLU                  ReLU
    19   'conv4-BN'             Batch Normalization   Batch normalization with 128 channels
    20   'conv4-maxpool'        Max Pooling           2×1 max pooling with stride [2  1] and padding [0  0  0  0]
    21   'conv4-dropout'        Dropout               25% dropout
    22   'conv5'                Convolution           256 64×1×128 convolutions with stride [1  1] and padding 'same'
    23   'conv5_relu'           ReLU                  ReLU
    24   'conv5-BN'             Batch Normalization   Batch normalization with 256 channels
    25   'conv5-maxpool'        Max Pooling           2×1 max pooling with stride [2  1] and padding [0  0  0  0]
    26   'conv5-dropout'        Dropout               25% dropout
    27   'conv6'                Convolution           512 64×1×256 convolutions with stride [1  1] and padding 'same'
    28   'conv6_relu'           ReLU                  ReLU
    29   'conv6-BN'             Batch Normalization   Batch normalization with 512 channels
    30   'conv6-maxpool'        Max Pooling           2×1 max pooling with stride [2  1] and padding [0  0  0  0]
    31   'conv6-dropout'        Dropout               25% dropout
    32   'classifier'           Fully Connected       360 fully connected layer
    33   'classifier_sigmoid'   Sigmoid               sigmoid
    34   'pitch'                Regression Output     mean-squared-error

    Use analyzeNetwork (Deep Learning Toolbox) to visually explore the network.

    analyzeNetwork(net)

    networkAnalyzerCREPE.png

    This example uses:

    Open Live Script

    The CREPE network requires you to preprocess your audio signals to generate buffered, overlapped, and normalized audio frames that can be used as input to the network. This example walks through audio preprocessing using crepePreprocess and audio postprocessing with pitch estimation using crepePostprocess. The pitchnn function performs these steps for you.

    Read in an audio signal for pitch estimation. Visualize and listen to the audio. There are nine vocal utterances in the audio clip. 

    [audioIn,fs] = audioread('SingingAMajor-16-mono-18secs.ogg');
soundsc(audioIn,fs)
T = 1/fs;
t = 0:T:(length(audioIn)*T) - T;
plot(t,audioIn);
grid on
axis tight
xlabel('Time (s)')
ylabel('Ampltiude')
title('Singing in A Major')

    Use crepePreprocess to partition the audio into frames of 1024 samples with an 85% overlap between consecutive mel spectrograms. Place the frames along the fourth dimension.

    [frames,loc] = crepePreprocess(audioIn,fs);

    Create a CREPE network with ModelCapacity set to tiny. If you call crepe before downloading the model, an error is printed to the Command Window with a download link.

    netTiny = crepe('ModelCapacity','tiny');

    Predict the network activations.

    activationsTiny = predict(netTiny,frames);

    Use crepePostprocess to produce the fundamental frequency pitch estimation in Hz. Disable confidence thresholding by setting ConfidenceThreshold to 0. 

    f0Tiny = crepePostprocess(activationsTiny,'ConfidenceThreshold',0);

    Visualize the pitch estimation over time.

    plot(loc,f0Tiny)
grid on
axis tight
xlabel('Time (s)')
ylabel('Pitch Estimation (Hz)')
title('CREPE Network Frequency Estimate - Thresholding Disabled')

    With confidence thresholding disabled, crepePostprocess provides a pitch estimate for every frame. Increase the ConfidenceThreshold to 0.8.

    f0Tiny = crepePostprocess(activationsTiny,'ConfidenceThreshold',0.8);

    Visualize the pitch estimation over time.

    plot(loc,f0Tiny,'LineWidth',3)
grid on
axis tight
xlabel('Time (s)')
ylabel('Pitch Estimation (Hz)')
title('CREPE Network Frequency Estimate - Thresholding Enabled')

    Create a new CREPE network with ModelCapacity set to full.

    netFull = crepe('ModelCapacity','full');

    Predict the network activations.

    activationsFull = predict(netFull,frames);
f0Full = crepePostprocess(activationsFull,'ConfidenceThreshold',0.8);

    Visualize the pitch estimation. There are nine primary pitch estimation groupings, each group corresponding with one of the nine vocal utterances.

    plot(loc,f0Full,'LineWidth',3)
grid on
xlabel('Time (s)')
ylabel('Pitch Estimation (Hz)')
title('CREPE Network Frequency Estimate - Full')

    Find the time elements corresponding to the last vocal utterance.

    roundedLocVec = round(loc,2);
lastUtteranceBegin = find(roundedLocVec == 16);
lastUtteranceEnd = find(roundedLocVec == 18);

    For simplicity, take the most frequently occurring pitch estimate within the utterance group as the fundamental frequency estimate for that timespan. Generate a pure tone with a frequency matching the pitch estimate for the last vocal utterance. 

    lastUtteranceEstimation = mode(f0Full(lastUtteranceBegin:lastUtteranceEnd))
    lastUtteranceEstimation = single
    217.2709

    The value for lastUtteranceEstimate of 217.3 Hz. corresponds to the note A3. Overlay the synthesized tone on the last vocal utterance to audibly compare the two. 

    lastVocalUtterance = audioIn(fs*16:fs*18);
newTime = 0:T:2;
compareTone = cos(2*pi*lastUtteranceEstimation*newTime).';

soundsc(lastVocalUtterance + compareTone,fs);

    Call spectrogram to more closely inspect the frequency content of the singing. Use a frame size of 250 samples and an overlap of 225 samples or 90%. Use 4096 DFT points for the transform. The spectrogram reveals that the vocal recording is actually a set of complex harmonic tones composed of multiple frequencies. 

    spectrogram(audioIn,250,225,4096,fs,'yaxis')

    Input Arguments

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    Model capacity, specified as the comma-separated pair consisting of 'ModelCapacity' and 'tiny', 'small', 'medium', 'large', or 'full'.

    Tip

    'ModelCapacity' controls the complexity of the underlying deep learning neural network. The higher the model capacity, the greater the number of nodes and layers in the model. Selecting the right model capacity for your data will help prevent under or overfitting.

    Data Types: string | char

    Output Arguments

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    Pretrained CREPE neural network, returned as a DAGNetwork (Deep Learning Toolbox) object.

    References

    [1] Kim, Jong Wook, Justin Salamon, Peter Li, and Juan Pablo Bello. “Crepe: A Convolutional Representation for Pitch Estimation.” In 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 161–65. Calgary, AB: IEEE, 2018. https://doi.org/10.1109/ICASSP.2018.8461329.

    Extended Capabilities

    GPU Arrays
    Accelerate code by running on a graphics processing unit (GPU) using Parallel Computing Toolbox™.

    Version History

    Introduced in R2021a

    See Also

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