Sound Source Localization

Sound source localization (SSL) aims to determine the location of sound sources using audio data from multiple microphones or binaural cues, a crucial task in fields like robotics and audio scene analysis. Current research emphasizes improving SSL accuracy and robustness in challenging environments (e.g., reverberation, noise) using deep learning models such as convolutional recurrent neural networks, masked autoencoders, and graph neural networks, often incorporating time-frequency analysis and cross-modal information (audio-visual). Advances in SSL have significant implications for applications ranging from improved speech enhancement and separation to more sophisticated human-computer interaction and assistive technologies for individuals with hearing impairments.