Multi Channel Audio

Multi-channel audio processing focuses on extracting information and enhancing audio quality from recordings using multiple microphones. Current research emphasizes developing robust algorithms and neural network architectures, such as variations of convolutional neural networks, recurrent neural networks (like Conformers), and autoencoders, to address challenges like sound source localization, speech separation in noisy environments, and efficient compression of multi-channel data. These advancements are crucial for improving applications ranging from speech recognition and audio conferencing to immersive audio experiences and underwater acoustic sensing. The field is actively exploring self-supervised learning techniques to leverage unlabeled data and improve model generalization.