Audio Augmentation

Audio augmentation involves artificially modifying audio data to increase the diversity and robustness of training datasets for machine learning models, primarily aiming to improve model performance and generalization. Current research focuses on developing effective augmentation strategies, including noise addition, pitch shifting, and the use of generative models to create synthetic audio, often in conjunction with self-supervised learning frameworks and architectures like Swin Transformers and convolutional neural networks. These advancements are significant for various applications, such as improving speech recognition in low-resource languages, enhancing audio fingerprinting accuracy in noisy environments, and advancing medical diagnosis using health-related acoustic signals.