The article explores the application of machine learning and deep learning techniques in audio analysis, focusing on the use of Comet's platform to facilitate these tasks. It highlights the importance of audio analysis in deep learning, detailing key methods and tools such as Librosa for audio feature extraction and the computation of Mel Frequency Cepstral Coefficients (MFCCs). The article provides a detailed walkthrough of preprocessing, feature extraction, and model training using the UrbanSound8k dataset, demonstrating how to build a neural network for audio classification. It further illustrates the use of Comet to track experiments and visualize results, emphasizing the platform's utility in improving model performance through features like hyperparameter optimization. The study concludes with an evaluation of the model's training and testing accuracy, noting areas for potential enhancement.