Generative Adversarial Network for Audio Generation

Classifying audio data presents a significant challenge due to the diverse nature of sounds and the complexities involved in distinguishing between them. In applications such as environmental monitoring, security, healthcare, and entertainment, the ability to automatically recognize and classify sounds is becoming increasingly important. However, current systems often struggle with handling large, unstructured audio datasets, requiring efficient feature extraction and classification methods. This project addresses the need for robust audio classification models by focusing on extracting meaningful features from raw audio files. By using the Freesound Dataset, we implemented a technique to extract Mel-frequency cepstral coefficients (MFCCs), which transform raw audio into a format suitable for machine learning. With this approach, we have developed a system capable of accurately classifying different types of sounds, paving the way for more advanced and practical audio recognition applications.