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In this work, we present a single-channel speech enhancement technique called the Modified Phase Opponency (MPO) model as a solution to the Speech Separation Challenge. The MPO model is based on a neural model for detection of tones-in-noise called the Phase Opponency (PO) model. Replacing the noisy speech signals by the corresponding MPO-processed signals increases the accuracy by 31% when the speech signals are corrupted by speech-shaped noise at 0 dB Signal-to-Noise Ratio (SNR). It is worth mentioning that the MPO enhancement scheme was developed using the noisy connected-digit Aurora database and was not tailored in any way to fit the Grid database used in this challenge. One of the salient features of the MPO-based speech enhancement scheme is that it does not need to estimate the noise characteristics, nor does it assume that the noise satisfies any statistical model.