  Traditionally, Blind Speech Separation techniques are computationally
expensive as they update the demixing matrix at every time frame index, making
them impractical to use in many Real-Time applications. In this paper, a robust
data-driven two-microphone sound source localization method is used as a
criterion to reduce the computational complexity of the Independent Vector
Analysis (IVA) Blind Speech Separation (BSS) method. IVA is used to separate
convolutedly mixed speech and noise sources. The practical feasibility of the
proposed method is proved by implementing it on a smartphone device to separate
speech and noise in Real-World scenarios for Hearing-Aid applications. The
experimental results with objective and subjective tests reveal the practical
usability of the developed method in many real-world applications.
