Vocal dereverberation remains a challenging task in audio processing, particularly for real-time applications where both accuracy and efficiency are crucial. Traditional deep learning approaches often struggle to suppress reverberation without degrading vocal clarity, while recent methods that jointly predict magnitude and phase have significant computational cost. We propose a real-time dereverberation framework based on residual mask prediction in the short-time Fourier transform (STFT) domain. A U-Net architecture is trained to estimate a residual reverberation mask that suppresses late reflections while preserving direct speech components. A hybrid objective combining binary cross-entropy, residual magnitude reconstruction, and time-domain consistency further encourages both accurate suppression and perceptual quality. Together, these components enable low-latency dereverberation suitable for real-world speech and singing applications.

翻译：语音去混响在音频处理中仍是一项具有挑战性的任务，特别是在对准确性和效率均有严格要求的实时应用中。传统的深度学习方法往往难以在抑制混响的同时保持语音清晰度，而近期联合预测幅度与相位的方法则存在显著的计算开销。本文提出一种基于短时傅里叶变换域残差掩蔽预测的实时去混响框架。通过训练U-Net架构来估计残差混响掩蔽，该掩蔽在抑制后期反射声的同时保留直达语音成分。结合二值交叉熵、残差幅度重建与时域一致性的混合目标函数，进一步促进了准确抑制与感知质量的提升。这些组件共同实现了适用于真实场景语音与歌唱应用的低延迟去混响系统。