The aim of this work is to perform polyphonic music transcription in an efficient way. The problem is formulated as a linear model and the speed is improved by a randomized SVD-based method. The method is shown to compete with the best resulting approaches in literature. The conventional methods seem to fail in this era of big data whereas the proposed method efficiently handles this by use of randomized algorithms for matrix decompositions. The method is able to improve time and space complexity without compromising the high success rate.