Neural-network based interpolation of late reverberation in coupled spaces using the common slopes model

Six-degrees-of-freedom audio rendering for eXtended Reality (XR) from limited measurements remains a challenging problem, particularly in coupled spaces where anisotropic and multi-slope late reverberation occurs. In this work, we adopt the common slopes model to represent position-dependent, directional late reverberation in a coupled space. Fourier-encoded positional coordinates are used as input to a Multi-Layer Perceptron (MLP) to predict position-dependent common-slope amplitudes in the spherical harmonic (SH) domain. The MLP is trained using a directional energy decay curve (DEDC) loss. At inference, the MLP predicts directional amplitudes of the DEDCs at unseen positions. These amplitudes, combined with the pre-determined common decay times, can drive a modal or shaped white noise reverberator to synthesise directional late reverberation tails at new locations. We demonstrate binaural rendering for 6DoF navigation and evaluate our approach on a simulated three-room dataset. Results show that an SRIR grid spacing of 60 cm is sufficient to obtain a mean EDC error below 1.3 dB. We also compare our method to the Neural Acoustical Field approach and achieve significantly faster inference with comparable EDC mismatch errors.