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There are abundant real time applications for singing voice separation from mixed audio. By means of Robust Principal Component Analysis (RPCA) which is a compositional model for segregation, which decomposes the mixed source audio signal into low rank and sparse components, where it is presumed that musical accompaniment as low rank subspace since musical signal model is repetitive in character while singing voices can be treated as moderately sparse in nature within the song. We propose an efficient optimization algorithm called as Augmented lagrange Multiplier designed to solve robust low dimensional projections. Performance evaluation of the system is verified with the help of performance measurement parameter such as source to distortion ratio(SDR),source to artifact ratio(SAR), source to interference ratio(SIR) and Global Normalized source to Distortion Ratio (GNSDR).KEYWORDS: Robust Principle Component Analysis (RPCA),Singing Voice Separation, Augmented Lagrange Multiplier (ALM),low rank matrix,sparse matrix. I.INTRODUCTIONNumerous classes of information are composed as constructive mixtures of portions. Constructive combination, is additive combination that do not result in deduction or diminishment of any of the portion of information, this is referred to as "compositional" data. To characterize such information, various mathematical models are developed. Such models have provided new standards to solve audio processing problems, such as blind and supervised source separation and robust recognition. Compositional models are used in audio processing systems to advance the state of the art on many difficulties that deal with audio data involving of multiple sources, for example on the analysis of polyphonic music and recognition of noisy speech. Thus we use robust principal component analysis (RPCA) as a compositional model in this paper.Robust Principal Component Analysis (RPCA) method is extensively used in the field of image processing for image segmentation, surveillance video processing, batch image alignment etc. This procedure has received recent prominence in the field of audio separation for the application of singer identification, musical information retrieval, lyric recognition and alignment.A song usually comprises of mixture of human vocal and musical instrumental audio pieces from string and percussion instruments etc. Our area of interest is segregating vocal line from music which is complex and vital musical signal element from song, thus we can treat musical as intrusion or noise with respect to singing voice. Human auditory system has incredible potential in splitting singing voices from background music accompaniment. This task is natural and effortless for humans, but it turns out to be difficult for machines [15].Compositional model based RPCA has emerged as a potential method for singing voice separation based on the notion that low rank subspace can be assumed to comprise of repetitive musical accompaniment, whereas the singing voice is relatively sparse in time frequency...