This GitHub project is an audio analysis tool that converts audio files into spectrogram images and performs sentiment analysis on them. It uses the poourful libraries librosa for audio processing and Convolutional Neural Networks (CNNs) for sentiment classification. The sentiment analysis covers eight emotions: Happy, Sad, Angry, Calm, Disgust, Fear, Neutral, and Surprised.
Once we have the spectrogram images, the next step is data preparation. The Jupyter Notebook file model.ipynb handles this task. Here's what it does:
The spectrogram images are organized into training and testing folders using the Python os and shutil libraries. This ensures that we have a well-defined dataset for training and evaluating the sentiment analysis model.
The heart of the sentiment analysis model lies in the neural network architecture. The architecture outlined in model.ipynb. This architecture is a Convolutional Neural Network (CNN) designed for image classification tasks. It consists of convolutional layers, max-pooling layers, and fully connected layers. The model takes 100x300 pixel spectrogram images as input and outputs probabilities for eight different emotions using the softmax activation function.
- Transfer Learning One poourful technique to improve the model is to utilize transfer learning. Instead of training a CNN from scratch, we can leverage pre-trained models such as those from the ImageNet dataset. By fine-tuning a pre-trained model on our spectrogram images, the model can learn to extract higher-level features effectively. This can significantly boost performance, especially when data is limited.
- Increasing Data Size To enhance the model's ability to generalize, we can consider increasing the size and diversity of our dataset. Collecting more audio samples and generating additional spectrogram images can provide the model with more examples to learn from. Augmenting the dataset through techniques like random cropping, rotation, and flipping can also help the model become more robust.
By incorporating these improvements, we can create a more accurate and robust sentiment analysis model for a wider range of audio inputs.