crepe
crepe
is not recommended. Use the audioPretrainedNetwork
function instead.
Description
Examples
Download pitchnn
Functionality
Download and unzip the Audio Toolbox™ model for CREPE to use pitchnn
.
Type pitchnn
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.mathworks.com/supportfiles/audio/crepe.zip'); crepeLocation = tempdir; unzip(loc,crepeLocation) addpath(fullfile(crepeLocation,'crepe'))
Load Pretrained CREPE Network
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)
Estimate Pitch Using CREPE Network
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
.
netTiny = audioPretrainedNetwork("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 = audioPretrainedNetwork("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))
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
CAP
— Model Capacity
'full'
(default) | 'tiny'
| 'small'
| 'medium'
| 'large'
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
net
— Pretrained CREPE neural network
DAGNetwork
object
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
C/C++ Code Generation
Generate C and C++ code using MATLAB® Coder™.
Usage notes and limitations:
To create a
SeriesNetwork
orDAGNetwork
object for code generation, see Load Pretrained Networks for Code Generation (MATLAB Coder).
GPU Code Generation
Generate CUDA® code for NVIDIA® GPUs using GPU Coder™.
Usage notes and limitations:
To create a
SeriesNetwork
orDAGNetwork
object for code generation, see Load Pretrained Networks for Code Generation (GPU Coder).
GPU Arrays
Accelerate code by running on a graphics processing unit (GPU) using Parallel Computing Toolbox™.
Version History
Introduced in R2021a
See Also
audioPretrainedNetwork
| crepePreprocess
| pitchnn
| crepePostprocess
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