The music industry has come a long way since its in‐ ception. Music producers have also adhered to modern technology to infuse life into their creations. Systems ca‐ pable of separating sounds based on sources especially vocals from songs have always been a necessity which has gained attention from researchers as well. The chal‐ lenge of vocal separation elevates even more in the case of the multi‐instrument environment. It is essential for a system to be first able to detect that whether a piece of music contains vocals or not prior to attempting source separation. It is also very much challenging to perform source separation from audio which is contaminated with noise. In this paper, such a system is proposed being tes‐ ted on a database of more than 99 hours of instrumen‐ tals and songs. Experiments were performed with both noise free as well as noisy audio clips. Using line spectral frequency‐based features, we have obtained the highest accuracies of 99.78% and 99.34% (noise free and noisy scenario respectively) from among six different classi‐ fiers, viz. BayesNet, Support Vector Machine, Multi Layer Perceptron, LibLinear, Simple Logistic and Decision Table.
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