Real World Sound

Real-world sound research focuses on understanding and modeling the complex acoustic environments we experience daily, aiming to improve sound recognition, synthesis, and analysis. Current efforts utilize deep learning models, including convolutional neural networks (CNNs) and recurrent neural networks (RNNs) like BiLSTMs, along with transformer-based architectures, to analyze and generate sounds, often leveraging large datasets and transfer learning techniques across different audio domains. This work has implications for diverse applications, such as improving assistive technologies for the elderly, enhancing audio classification in smart home devices, and advancing the understanding of human perception and communication through sound.