Confusion Matrix
Description
Confusion matrices of the five algorithms: CNN, KNN, SVM, LSTM, and Random Forest on the sound classification using the NIGENS dataset.
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Steps to reproduce
The methodology begins with data collection using the NIGENS dataset. Next, in the data pre-processing stage, features such as MFCC, Chroma, and Mel are extracted, and data augmentation techniques like time shifting, pitch shifting, adding noise, and time stretching are applied. The data is then normalized to ensure consistency. The dataset is split into a training set (60%) and a testing set (40%) to prepare for model evaluation. The model training process involves five algorithms: CNN, KNN, SVM, LSTM, and Random Forest. After training, the models are evaluated using the testing set. Finally, the performance of the models is compared based on accuracy, precision, recall, F1-score, and confusion matrices to identify the best-performing algorithm for sound classification using the NIGENS dataset.