  Neural network based speech dereverberation has achieved promising results in
recent studies. Nevertheless, many are focused on recovery of only the direct
path sound and early reflections, which could be beneficial to speech
perception, are discarded. The performance of a model trained to recover clean
speech degrades when evaluated on early reverberation targets, and vice versa.
This paper proposes a novel deep neural network based multichannel speech
dereverberation algorithm, in which the dereverberation level is controllable.
This is realized by adding a simple floating-point number as target controller
of the model. Experiments are conducted using spatially distributed
microphones, and the efficacy of the proposed algorithm is confirmed in various
simulated conditions.
