Songs are representation of audio signal and musical instruments. An audio signal separation system should be able to identify different audio signals such as speech, background noise and music. In a song the singing voice provides useful information regarding pitch range, music content, music tempo and rhythm. An automatic singing voice separation system is used for attenuating or removing the music accompaniment. The paper presents survey of the various algorithm and method for separating singing voice from musical background. From the survey it is observed that most of researchers used Robust Principal Component Analysis method for separation of singing voice from music background, by taking into account the rank of music accompaniment and the sparsity of singing voices.
An audio signal is a representation of sound. Audio signals have frequency range 20 to 20 kHz. Audio signals may be synthesized directly. A mixture refers to the physical combination of two or more substances on which the identities and are mixed in the form to separate out. An audio signal classification system should be able to categorize different audio input formats (speech, background noise, and music). Audio signal classification system analyzes the input audio signal and describes the signal at the output. These are used to characterize both music and speech signals. The categorization can be done on the basis of pitch, music content, music tempo and rhythm. From the comparative results it is observed that the wiener filter is better for noise reduction than others. We refer SEGSNR parameter for study because of its improved filter performance. Separating singing voice from music is very useful in many applications.
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