Multi Channel

Multi-channel processing focuses on analyzing and interpreting data from multiple sources simultaneously, leveraging the combined information for improved performance over single-channel approaches. Current research emphasizes the development of robust and efficient algorithms, including neural networks (e.g., conformers, CNNs) and deep reinforcement learning, to handle diverse data types (audio, visual, biological signals) and address challenges like noise reduction, data scarcity, and efficient representation learning. This field is crucial for advancements in various applications, such as improved speech recognition in noisy environments, medical diagnosis using multi-channel biosignals, and enhanced performance in communication systems.