Multi-window comparison of SIR performance in extraction of mono-aural vocal and non-vocal components in REPET

The distinction of the lead varying vocals from the background music in an audio recording is an extremely demanding and exigent task. The speech-separation research usually inculcates Time-frequency masking technique that ultimately appraises the hearing-aid design. The core principle in music which is capitalized to discriminate underlying non-vocals from vocals (speech) is Repetition. The `Repetition' feature is especially enacted for pop songs where the singer often overlays frequently changing vocals on a periodically repeating background in a mixture. The basic approach of this research paper is the recognisation of periodically repeating segments in audio excerpts, analogize them with a repeating model and finally discrimate the repeating musical patterns via Time-Frequency masking. A TF mask is grounded on the basis of TF representation of any signal. In the proposed algorithm, the quality of foreground vocals and accompanying background can be adjudicated in terms of SIR (Signal to Interference Ratio) value utilizing `ANOVA' (Analysis Of Variation) computational method on six different genres of musical audios and formulated the complete comparison of SIR values using hamming, hanning and blackmann windows and concluded that separation of mono-aural vocal and non-vocal components applying blackmann window shows better SIR values as compared to hanning and hamming windows.