Multi Channel Input

Multi-channel input processing focuses on leveraging information from multiple data streams (e.g., microphones, sensors) to improve performance in various applications. Current research emphasizes developing robust algorithms and neural network architectures, such as attention-based beamformers and variants of nonnegative matrix factorization, to handle diverse data types and challenging scenarios like moving speakers or unknown numbers of sources. These advancements are significantly impacting fields ranging from speech enhancement and automatic speech recognition to power transformer diagnostics and physical system modeling, enabling more accurate and efficient data analysis. The development of invariant features for multi-channel data is also a growing area of interest.