Deep Learning-Based Acoustic Classification of Animal Species Using Convolutional Neural Networks

The project explores the identification of animal species through sound acoustics, employing deep learning methods for classifying animal vocalizations. The workflow includes loading and preprocessing audio samples from diverse species, such as lions, bears, cats, and dogs. Some of the essential audio features like Mel-Frequency Cepstral Coefficients (MFCCs), Mel spectrograms and chroma are extracted from the audio data to train the neural network. A Convolutional Neural Network (CNN) is applied for the classification task after splitting the dataset into training and testing subsets. The model architecture includes multiple convolutional layers and pooling layers, which is followed by fully connected layers that generate a probability distribution over the various animal categories. The training process is monitored through accuracy metrics. Upon completion, the model demonstrates high accuracy in distinguishing sounds from different animals. The trained model is then saved for future use, enabling predictions on new audio files. A dedicated function is implemented to identify the animal species from a provided sound file, showcasing the model's ability to classify new sound inputs accurately. The system has practical applications in scenarios requiring animal sound classification.