Audio Classification Model

Overview

This project involves building an audio classification model using machine learning techniques. The model is designed to classify audio signals into predefined categories based on their features.

Dataset

The dataset used for training and testing the model consists of labeled audio files. Each audio file is associated with a specific class label, representing the category it belongs to.

Preprocessing

1. Audio Loading: Audio files are loaded and converted into a consistent format.
2. Feature Extraction: Key features such as Mel-frequency cepstral coefficients (MFCCs), chroma features, and spectral contrast are extracted from the audio signals.
3. Normalization: The extracted features are normalized to ensure uniformity across the dataset.

Model Architecture

The model is built using a deep learning framework, leveraging a convolutional neural network (CNN) for feature learning and classification. The architecture includes:

• Input Layer: Accepts the preprocessed audio features.
• Convolutional Layers: Extracts spatial hierarchies of features.
• Pooling Layers: Reduces the dimensionality of the feature maps.
• Fully Connected Layers: Performs the final classification based on the learned features.
• Output Layer: Produces the probability distribution over the classes.

Training

The model is trained using a supervised learning approach. The dataset is split into training and validation sets. The training process involves:

• Loss Function: Categorical cross-entropy is used as the loss function.
• Optimizer: Adam optimizer is employed to minimize the loss.
• Evaluation Metrics: Accuracy, precision, recall, and F1-score are used to evaluate the model's performance.

Results

The trained model achieves high accuracy on the validation set, demonstrating its effectiveness in classifying audio signals. Detailed performance metrics and confusion matrix are provided in the results section.

Usage

To use the model for audio classification:

1. Load the Model: Load the pre-trained model from the provided file.
2. Preprocess Audio: Preprocess the input audio file to extract features.
3. Predict: Use the model to predict the class of the audio signal.

Conclusion

This audio classification model provides a robust solution for categorizing audio signals. Future work includes exploring more advanced architectures and larger datasets to further improve performance.