  For real-time speech enhancement (SE) including noise suppression,
dereverberation and acoustic echo cancellation, the time-variance of the audio
signals becomes a severe challenge. The causality and memory usage limit that
only the historical information can be used for the system to capture the
time-variant characteristics. We propose to adaptively change the receptive
field according to the input signal in deep neural network based SE model.
Specifically, in an encoder-decoder framework, a dynamic attention span
mechanism is introduced to all the attention modules for controlling the size
of historical content used for processing the current frame. Experimental
results verify that this dynamic mechanism can better track time-variant
factors and capture speech-related characteristics, benefiting to both
interference removing and speech quality retaining.
