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The human voice is singular because it has a specific frequency spectrum and fast transitions between vowels and consonants.

What are good and computationally fast ways to detect if there is probably a voice contained in a noisy music, the amount of the song where there is vocals, and the type of vocals in different kinds of music, that perhaps have beats and guitars and other sounds mixed into the vocal?

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Scalable audio separation with light Kernel Additive Modelling

http://www.loria.fr/~aliutkus/kaml/

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  • $\begingroup$ Any chance for getting a descriptive answer? $\endgroup$ – jojek May 26 '15 at 11:52
  • $\begingroup$ In addition it's hardly addressing the question. Vocal detection and source separation are only superficially related. $\endgroup$ – Jazzmaniac May 26 '15 at 13:53
  • $\begingroup$ The linked tool separates vocals from beats and other music based on spectral patterns of those, it is exactly what author needs. You can also check examples here loria.fr/~aliutkus/kam $\endgroup$ – Nikolay Shmyrev May 26 '15 at 15:09
  • $\begingroup$ @NikolayShmyrev, either you or me are misunderstanding the original question. In my eyes what he needs is not any form of separation, but merely presence detection of voice. Separating different sources provides little help there and overcomplicates the whole process. Even with separate sources, you'd still have to find some sort of classifier that tells you if one of these sources is voice. There is no reason to assume that such a classifier could not operate on the mixed source signal. In fact, a separation of different vocal features into different sources might make it harder. $\endgroup$ – Jazzmaniac May 26 '15 at 18:11
  • $\begingroup$ Dear @Jazzmaniac, if you are interested in this technology you are welcome to visit the link and check the publication to learn how it finds voice source based on voice spectrotemporal properties. Method also detects beat sources and melody sources. The classifier is not required. $\endgroup$ – Nikolay Shmyrev May 26 '15 at 19:26

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