Sound Source Distance

Accurately estimating the distance of a sound source is a challenging problem with applications in diverse fields like virtual reality and robotics. Current research focuses on improving the accuracy of distance estimation, particularly in complex acoustic environments, using deep learning models such as convolutional recurrent neural networks (CRNNs) and relation networks. These models are trained on simulated and real-world audio data, often addressing the mismatch between training and testing conditions through techniques like stochastic room reverberation modeling. Improved accuracy in sound source distance estimation has significant implications for enhancing the realism of virtual auditory environments and improving the performance of sound localization systems.