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In this work, we present a two-stage method for speaker extraction underreverberant and noisy conditions. Given a reference signal of the desiredspeaker, the clean, but the still reverberant, desired speaker is firstextracted from the noisy-mixed signal. In the second stage, the extractedsignal is further enhanced by joint dereverberation and residual noise andinterference reduction. The proposed architecture comprises two sub-networks,one for the extraction task and the second for the dereverberation task. Wepresent a training strategy for this architecture and show that the performanceof the proposed method is on par with other state-of-the-art (SOTA) methodswhen applied to the WHAMR! dataset. Furthermore, we present a new dataset withmore realistic adverse acoustic conditions and show that our method outperformsthe competing methods when applied to this dataset as well.

A two-stage speaker extraction algorithm under adverse acoustic conditions using a single-microphone