Hi Joel,
Your project is tackling a complex and socially impactful problem, and it sounds like you've made significant progress. The challenge of accurately classifying the 'two' category, given its subtlety, is indeed tricky. Based on the information you've provided, here are a few suggestions on how to approach the classification of the 'two' category, including the potential use of a Random Forest classifier:
Step 1: Analyze biLSTM Output
First, thoroughly analyze the output of your biLSTM network for the 'two' files. You're looking for patterns or characteristics in the segments classified as 'one' or 'three'. This might involve:
- Calculating the percentage of segments classified as 'three' for each file.
- Assessing the distribution of scores across segments within each file.
Step 2: Feature Extraction for Meta-Classifier
Based on your analysis, extract features that could help differentiate 'two' from 'one' and 'three'. Possible features might include:
- The percentage of segments classified as 'three'.
- The variability or standard deviation in segment classifications within a file.
- Any temporal patterns in the classifications (e.g., sequences of 'three' classifications).
You can use MATLAB's built-in functions for statistical calculations to extract these features from your biLSTM's output.
Step 3: Prepare the Dataset
Prepare a dataset for training your Random Forest where each instance represents a file, using the features extracted in Step 2. Label the dataset based on the known classifications ('one', 'two', 'three').
Step 4: Train a Random Forest Classifier
Utilize MATLAB's TreeBagger function to train a Random Forest classifier. The TreeBagger function is part of MATLAB's Statistics and Machine Learning Toolbox and is well-suited for classification tasks. Here's a simplified example of how to use TreeBagger:
RFModel = TreeBagger(50, X, Y, 'Method', 'classification');
In this example, 50 denotes the number of trees in the forest, X is your feature matrix where each row is an observation (file) and each column is a feature, and Y is the vector of labels ('one', 'two', 'three') for each observation. You can read about it more on this: https://in.mathworks.com/help/stats/treebagger.html Step 5: Validate and Adjust
After training your Random Forest model, validate its performance using a separate validation set or through cross-validation. Evaluate the model's ability to correctly classify the 'two' category, and adjust your feature set or model parameters as necessary to optimize performance.
Step 6: Integration and Testing
Integrate the Random Forest classifier with your existing workflow. This might involve:
- Running your audio files through the biLSTM network to get the initial segment classifications.
- Extracting the features from these classifications for each file.
- Using the trained Random Forest model to classify each file as 'one', 'two', or 'three'.
Additional Tips
- Feature Engineering: Spend time on feature engineering based on the biLSTM output. The quality and creativity of your features can significantly impact the performance of your Random Forest model.
- Model Tuning: Experiment with different parameters for the Random Forest (TreeBagger options) and the number of trees to find the best model.
- Cross-Validation: Use MATLAB's cross-validation functions to assess the generalizability of your Random Forest model.
This approach leverages the strengths of deep learning for initial audio processing and feature extraction, while utilizing classical machine learning to handle the nuanced classification task, all within MATLAB's robust computational environment.