The invention discloses a light-weight binaural
speech enhancement method based on a Fourier network, which recovers pure speech from a noisy binaural
signal through a light-weight deep
complex network architecture. The method comprises the following steps: firstly, generating robust acoustic representation by utilizing a double-path
feature coding and fusion module and combining short-time
Fourier transform features and psychoacoustic-based Gammatone features; then, through a global adaptive Fourier modulator
backbone network, a long-time-sequence context is efficiently modeled with extremely low calculation cost, and meanwhile, phase information is kept through a real number gating mechanism; and finally, performing intelligent correction on an enhancement result through a dynamic optimization gate. According to the method, a long
time sequence dependence modeling task is converted into a
Fourier domain adaptive filtering process, so that the
speech enhancement performance and the spatial clue fidelity are maintained, the
model parameter quantity and the calculation complexity are reduced, and the problem that the performance and the efficiency of resource-constrained equipment are difficult to consider at the same time in the prior art is solved.