Deep learning-based method for acoustic feedback suppression in closed-loop system

Abstract

A deep learning-based method for acoustic feedback suppression in a closed-loop system, the method includes applying an offline trained closed-loop system suppression model, processing an audio signal input, and then feeding the processed audio signal to a sound reproduction unit of the closed-loop system for playback to achieve acoustic feedback suppression, the closed-loop system suppression model being built based on deep learning; and modeling the closed-loop system, generating a unit impulse response of an acoustic feedback path by simulation, and calculating a maximum stable gain according to each simulated unit impulse response, and generating a closed-loop signal based on the maximum stable gain; generating an open-loop target signal under an open-loop condition by using the audio signal input to the closed-loop system; forming parallel training data of the model by putting the closed-loop signal and open-loop target signal together, and training the model by using the generated parallel training data.