Using audio m4a files for training

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Aman
Aman 2024-1-20
回答： Hassaan 2024-1-21
% Train Classification Model from Audio Files
% Assuming you have a dataset with labeled audio files in 'audio_data' folder
audioData = audioDatastore('C:\Users\aman\Desktop\data\Training', 'IncludeSubfolders', true, 'LabelSource', 'Yes' );
% Feature extraction and preprocessing (you may need to customize this based on your audio features)
audioFeatures = @(x) melSpectrogramFeatureExtractor(x);
audioData.ReadFcn = audioFeatures;
% Split the dataset into training and validation sets
[trainData, validationData] = splitEachLabel(audioData, 0.8, 'randomized');
% Choose a classification model (e.g., SVM)
mdl = fitcecoc(trainData, 'Learners', 'linear', 'Coding', 'onevsall');
% Evaluate the model on the validation set
accuracy = evaluate(mdl, validationData);
% Display the accuracy
disp(['Validation Accuracy: ', num2str(accuracy)]);
% Live Audio Classification
% Set up audio input
audioInput = audioDeviceReader;
disp('Listening... Press Ctrl+C to stop.');
try
while true
% Acquire audio input
audioSignal = audioInput();
% Extract features from live audio
liveAudioFeatures = audioFeatures(audioSignal);
% Make prediction using the trained model
prediction = predict(mdl, liveAudioFeatures);
% Display the prediction
disp(['Prediction: ', prediction]);
end
catch
release(audioInput);
disp('Audio input stopped.');
end
% Feature extraction function (you may need to customize this based on your requirements)
function features = melSpectrogramFeatureExtractor(audio)
features = melSpectrogram(audio, 'NumBands', 40);
features = features(:)';
end
splitEachLabel(audioData, 0.8, 'randomized'); this is throwing error and community says to use cross validation,
how can i make this right so that multi label yes and no and multiclass male female can be trained with m4a file in folders

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回答（1 个）

Hassaan
Hassaan 2024-1-21
% Load the .mat file
audioData = audioDatastore('C:\Users\aman\Desktop\data\Training', ...
'IncludeSubfolders', true, ...
'FileExtensions', '.m4a', ...
'LabelSource', 'foldernames'); % Update based on your labeling strategy
% Define feature extraction function (update as needed)
audioData.ReadFcn = @(filename) extractFeaturesFromAudio(filename);
% Define a cross-validation partition strategy
cv = cvpartition(size(audioData.Labels, 1), 'KFold', 10); % Example for 10-fold cross-validation
% Placeholder for cross-validation loop (you need to implement training and validation within this loop)
for i = 1:cv.NumTestSets
trainIdx = cv.training(i);
valIdx = cv.test(i);
% Select training and validation data
trainData = subset(audioData, trainIdx);
valData = subset(audioData, valIdx);
% Train model and evaluate validation data
% ...
end
% Placeholder for feature extraction function
function features = extractFeaturesFromAudio(audioFilename)
% Read the audio file (update with actual feature extraction)
[audioIn, fs] = audioread(audioFilename);
features = melSpectrogram(audioIn, fs); % Example feature extraction
features = features(:)'; % Convert to row vector
end
This is a high-level guide and requires significant detail to be filled in based on your specific dataset and problem. It's important to align the feature extraction, labeling strategy, and classification model with the requirements of multi-label classification. Also, you'll need to ensure that your labels are structured in a way that each instance can have multiple labels associated with it.
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If you find the solution helpful and it resolves your issue, it would be greatly appreciated if you could accept the answer. Also, leaving an upvote and a comment are also wonderful ways to provide feedback.
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It's important to note that the advice and code are based on limited information and meant for educational purposes. Users should verify and adapt the code to their specific needs, ensuring compatibility and adherence to ethical standards.
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