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Implementation: I had taken inspiration from Kaggle Dataset, and implemented in Tensorflow 2.0.
- The proprocessing part, is loading the data, labelling the data, and then mixing the background noise with them, and then train the model, apply cross-validation technqiue, dimensionality reduction, and obtain the results.
- The model used are:
- Neural Network (Convolution Neural Network)
- Random Forest
- KNN
There are three notebooks:
- KNN-Project-Recognition.py contains the preprocessing part, and then applying the KNN Model, and performing cross validation and dimensionality reduction
- Random-Forest-Project-Recognition.py contains the preprocessing part, and then applying the Random Forest Model, and performing cross validation and dimensionality reduction
- the whole code for speech recognition (neural network and random forest).py contains the preprocessing part, and then applying the Tensorflow Model and Random Forest, and dimensionality reduction