Ideal binary time-frequency masking is a signal separation technique that retains mixture energy in time-frequency units where local signal-to-noise ratio exceeds a certain threshold and rejects mixture energy in other time-frequency units. Two experiments were designed to assess the effects of ideal binary masking on speech intelligibility of both normal-hearing (NH) and hearing-impaired (HI) listeners in different kinds of background interference. The results from Experiment 1 demonstrate that ideal binary masking leads to substantial reductions in speech-reception threshold for both NH and HI listeners, and the reduction is greater in a cafeteria background than in a speech-shaped noise. Furthermore, listeners with hearing loss benefit more than listeners with normal hearing, particularly for cafeteria noise, and ideal masking nearly equalizes the speech intelligibility performances of NH and HI listeners in noisy backgrounds. The results from Experiment 2 suggest that ideal binary masking in the low-frequency range yields larger intelligibility improvements than in the high-frequency range, especially for listeners with hearing loss. The findings from the two experiments have major implications for understanding speech perception in noise, computational auditory scene analysis, speech enhancement, and hearing aid design.