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Despite remarkable performance of Deep Learning based Acoustic Echo Cancellation (AEC) systems, effective handling of double-talk scenarios remains a challenge. During double-talk the speech signal from the far-end talker overlaps with the target near-end speech and results in degraded performance in form of near-end speech deletions or audible echo residuals shadowing the voice of target speaker. This paper introduces an approach to reduce the shadowing effect through altering the ground truth used for model training such that the model can effectively clean up spectral components where the interference is stronger than the target speech. The alteration is accomplished leveraging the availability of interference signal during training data generation by masking spectral components of the ground truth where target speech is significantly weaker than the interference. Large scale subjective evaluation trials show that human listeners prefer the outputs generated by the new approach.