Spatial Audio

Spatial audio studies how listeners and machines use sound’s direction, distance, motion, and reverberation, enabling immersive playback, source tracking, and sound-guided interaction. Core approaches include first- and higher-order Ambisonics, spherical-harmonic processing, microphone-array beamforming, and binaural rendering with head-related transfer functions (HRTFs). Neural fields and diffusion or flow models now support tasks such as room-response prediction, spatial upmixing, and scene-grounded audio generation, while audio-language models are being tested on localization and motion-reasoning tasks. A central challenge is making systems generalize from simulated rooms and fixed arrays to real environments while preserving perceptually important cues; benchmarks such as DCASE and LOCATA, alongside human listening tests, help expose gaps in both spatial accuracy and evaluation metrics.