Improved GSC Speech Enhancement Algorithm Based on Time-frequency Mask
JIANG Ren
Abstract：Aiming at the problem of insufficient performance of generalized sidelobe canceller(GSC)in low SNR environment and dynamic complex scenes,an improved GSC speech enhancement algorithm based on time-frequency mask is proposed.The al-gorithm uses the depth neural network speech enhancement system based on time-frequency mask to improve the post filtering sys-tem of GSC,uses the GSC improved by normalized minimum mean square error to filter out directional noise,and then uses pure speech time-frequency mask estimation to effectively retain speech components and suppress noise components.Through the percep-tual evaluation of speech quality(PESQ)and spectrogram analysis under different background noises,it is proved that the algo-rithm has advantages in low SNR,spectrogram is closer to pure speech,overall quality improvement of speech is good,and the pro-cessed speech is closer to the target speech.
Keywords：speech enhancementdeep neural networksbeamforminggeneralized sidelobe cancellerideal ratio mask
Publication Date：2025-11-20
Online Publishing Date：2026-01-28(First online date of this platform, not the publication date of the document)
Pages：7( 3032-3038 )