Overview
It is an integral part of our Scream Detection AI/ML model, which aims to enhance the safety and security of our society. This narrative will provide an overview of the goals, methods, and interpretation of our project
Dataset
The dataset consists of audio recordings labeled as containing screams or not containing screams. It is essential to have a balanced dataset with a sufficient number of positive (scream) and negative (non-scream) samples for effective model training. The dataset should also be split into training, validation, and test sets for model evaluation.
Model Architecture
The system utilizes ResNet, a deep residual neural network architecture, for audio classification. ResNet architectures are known for their ability to train very deep neural networks effectively, which makes them suitable for complex audio classification tasks.
Training
The model is trained using the training set from the dataset. The training process involves the following steps:
Data preprocessing: Audio samples are preprocessed and converted into suitable input formats for the model. Model training: The ResNet model is trained using the preprocessed audio samples. Loss optimization: The model is optimized using a suitable loss function, such as binary cross-entropy loss, and an optimization algorithm like Adam
To use the scream detection system:
Preprocess the audio samples to the required format. Load the trained ResNet model. Input the preprocessed audio samples into the model. Obtain the model predictions for scream detection. Future Improvements