Blind Source Separation techniques are widely used in the field of wireless communication for a very long time to extract signals of interest from a set of multiple signals without training data. In this paper, we investigate the problem of separation of the human voice from a mixture of human voice and sounds from different musical instruments. The human voice may be a singing voice in a song or may be a part of some news, broadcast by a channel with background music. This paper proposes a generalized Short Time Fourier Transform (STFT)-based technique, combined with filter bank to extract vocals from background music. The main purpose is to design a filter bank and to eliminate background aliasing errors with best reconstruction conditions, having approximated scaling factors. Stereo signals in time-frequency domain are used in experiments. The input stereo signals are processed in the form of frames and passed through the proposed STFT-based technique. The output of the STFT-based technique is passed through the filter bank to minimize the background aliasing errors. For reconstruction, first an inverse STFT is applied and then the signals are reconstructed by the OverLap-Add method to get the final output, containing vocals only. The experiments show that the proposed approach performs better than the other state-of-the-art approaches, in terms of Signal-to-Interference Ratio (SIR) and Signal-to-Distortion Ratio (SDR), respectively.