Hearing impaired (HI) persons often struggle with understanding speech in the presence of background noise even when wearing hearing assistive devices. The vast majority of noise reduction algorithms today are multiple-channel algorithms that use correlation between inputs to differentiate speech from noise. While significantly reducing background noise, these algorithms still result in a noisy signal that can affect HI persons ability to understand speech. The Cochlear Noise Reduction Algorithm (CNRA) is a single-channel binary mask algorithm which was tested on a group of HI subjects wearing their hearing assistive devices and was proven to significantly improve speech intelligibility. Massive computations are required to solve the model on which the CNRA depends, making its use in real-time applications a challenge. The purpose of this study is to design a variant of the CNRA that can perform in real-time on portable platforms, so that it can be used as the basic building block of a smartphone-based hearing-aid device. Such an implementation is presented and its results tested to prove accuracy.