RTF Vector Estimation

RTF vector estimation aims to accurately determine the relative transfer functions between microphones in multi-microphone systems, crucial for tasks like sound source localization in challenging acoustic environments. Current research focuses on improving the robustness of RTF estimation against noise and reverberation, employing techniques such as covariance whitening, spatial coherence-based methods, and deep learning approaches that leverage convolutional and recurrent neural networks to learn direct-path RTFs. These advancements are significant for applications such as hearing aids and acoustic sensor networks, enabling more accurate and computationally efficient sound source localization and potentially improving speech enhancement and noise reduction capabilities.