This project is aimed at building a machine learning model for classifying robot-generated voices into five different categories. The goal is to distinguish between various types of robot voices based on audio data.

The dataset used for training and evaluation consists of audio recordings of robot voices. Each voice belongs to one of the following five categories:

1. Category 0
2. Category 1
3. Category 2
4. Category 3
5. Category 4

The machine learning model employed for this classification task is a Convolutional Neural Network (CNN). The model architecture includes multiple convolutional layers followed by max-pooling, batch normalization, and dropout layers to prevent overfitting. The final layer utilizes a softmax activation function to output probabilities for each category.

To run this project, you will need the following:

• TensorFlow/Keras (for deep learning)
• NumPy (for numerical operations)
• Matplotlib (for plotting spectrograms)
• PyDub (for audio processing)
• Jupyter Notebook (optional, for running code cells interactively) ##Resukts We manged to achieve 85% accuracy based on unseen data of IEEE competition.