Multi Channel Speech Enhancement

Multi-channel speech enhancement aims to improve speech intelligibility in noisy environments by leveraging information from multiple microphones. Current research focuses on developing computationally efficient deep learning models, often incorporating techniques like attention mechanisms, Wiener filtering, and spherical harmonics transforms, to enhance speech while preserving spatial audio cues. These advancements are crucial for applications such as hearing aids, smart glasses, and meeting transcription systems, where robust and resource-efficient speech enhancement is essential for improved user experience and performance. The field is actively exploring optimal combinations of deep learning and traditional signal processing methods to achieve superior performance with minimal computational cost.