One major challenge of identifying singers in popular music recordings lies in how to reduce the interference of background accompaniment in trying to characterize the singer voice. Although a number of studies on automatic Singer IDentification (SID) from acoustic features have been reported, most systems to date, however, do not explicitly deal with the background accompaniment. This study proposes a background accompaniment removal approach for SID by exploiting the underlying relationships between solo singing voices and their accompanied versions in cepstrum. The relationships are characterized by a transformation estimated using a large set of accompanied singing generated by manually mixing solo singing with the accompaniments extracted from Karaoke VCDs. Such a transformation reflects the cepstrum variations of a singing voice before and after it is added with accompaniments. When an unknown accompanied voice is presented to our system, the transformation is performed to convert the cepstrum of the accompanied voice into a solo-voice-like one. Our experiments show that such a background removal approach improves the SID accuracy significantly; even when a test music recording involves sung language not covered in the data for estimating the transformation.

A prerequisite for identifying the singers in popular music recordings is to reduce the interference of background accompaniment when trying to characterize the singer voice. This study proposes a background music removal approach for singer identification (SID) by exploiting the underlying relationships between solo voices and their accompanied versions in cepstrum. The relationships are characterized by a transformation estimated using a large set of accompanied singing generated by manually mixing solo singing with the accompaniments extracted from Karaoke VCD. This transformation reflects the cepstrum variations of a singing voice before and after it is added with accompaniments. When an unknown accompanied voice is to be identified by our system, we convert its cepstrum into a solo-like one based on the pre-trained transformation. Our experiments show that such a background music removal approach improves the SID accuracy noticeably.