Spoken Digit Recognition with Wavelet Scattering Transform and Deep Learning Using MATLAB
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In this demonstration, we explore a comprehensive approach to spoken digit classification by harnessing the combined power of machine learning and deep learning techniques.
The example showcases the utilization of wavelet time scattering alongside a support vector machine (SVM) and a long short-term memory (LSTM) network for classification purposes.
Moreover, Bayesian optimization is employed to fine-tune hyperparameters and enhance the accuracy of the LSTM network.
Additionally, the example unveils an alternative approach employing a deep convolutional neural network (CNN) in conjunction with mel-frequency spectrograms.(MFCCs - Mel-frequency cepstral coefficients )
Data Preparation: Obtain a dataset of spoken digit audio samples. The dataset should be labeled, with each sample corresponding to a specific digit. Split the dataset into training and testing sets.
Wavelet Scattering: Apply wavelet time scattering to the audio samples. This involves decomposing the signals into multiple scales using wavelet transforms. Compute scattering coefficients that capture important temporal and spectral information.
Feature Extraction: Extract relevant features from the scattering coefficients. This can include statistical measures such as mean, variance, and spectral features like Mel-frequency cepstral coefficients (MFCCs). These features serve as input to the classification models.
Support Vector Machine (SVM): Train an SVM classifier using the extracted features from the scattering coefficients. Optimize the SVM hyperparameters, such as the kernel type and regularization parameter, using cross-validation.
Long Short-Term Memory (LSTM) Network: Build an LSTM network architecture for spoken digit classification. Design the network with appropriate input and output layers, along with LSTM layers for sequence modeling. Train the LSTM network using the extracted features, and optimize hyperparameters such as the number of LSTM units, learning rate, and dropout rate using Bayesian optimization.
Evaluation: Evaluate the performance of the SVM and LSTM models using the testing set. Calculate metrics such as accuracy, precision, recall, and F1 score to assess the models' classification performance.
Deep Convolutional Neural Network (CNN): Implement a deep CNN model for spoken digit recognition. Convert the audio samples into mel-frequency spectrograms as input to the CNN. Design the CNN architecture with convolutional, pooling, and fully connected layers. Train the CNN model using the mel-frequency spectrograms.
Model Comparison: Compare the performance of the SVM, LSTM, and CNN models. Assess their accuracy, computational efficiency, and robustness to variations in the spoken digit samples.
Conclusion: Summarize the findings and insights from the experimentation with wavelet scattering and deep learning techniques for spoken digit recognition. Reflect on the strengths and limitations of each approach and suggest potential areas for improvement.
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