Fast Algorithm for Moving Sound Source

Modern neural network-based speech processing systems need reverberation resistance, relying on large amounts of reverberation data for training. Existing methods simulate dynamic scenarios by sampling static systems or supplement with measured data, but struggle to simulate motion data conforming to physical laws. To address insufficient training data for speech enhancement models in moving scenarios, this paper proposes Yang's motion spatio-temporal sampling reconstruction theory, enabling efficient simulation of motion-induced continuous time-varying reverberation. It breaks through the limitations of traditional static Image-Source Method (ISM) in time-varying systems by decomposing the moving image source's impulse response into linear time-invariant modulation and discrete time-varying fractional delay, establishing a physics-compliant moving sound field model. Based on the band-limited nature of motion displacement, a hierarchical sampling strategy is adopted: high sampling rates for low-order images to retain details, and low rates for high-order ones to reduce complexity, combined with a fast synthesis architecture for real-time simulation. Experiments show that compared to open-source model GSound, the theory more accurately restores amplitude and phase changes in moving scenarios, solving the industry challenge of motion sound source data simulation. It provides high-quality dynamic training data for speech enhancement models and improves the robustness of multi-channel end-to-end voice tracking algorithms.