Singing voice timbre classification of Chinese popular music
Cheng-Ya Sha, Yi‐Hsuan Yang, Yu‐Ching Lin, Homer H. Chen · 2013
Singing voice plays an important role in the listening experience of music. In this paper, we propose to classify popular music by the timbre quality of the singing voice. Specifically, we adopt six singing voice timbre classes as the taxonomy and build a new data set, KKTIC, that contains the expert annotations of 387 Chinese popular songs. To build an automatic classifier, we resort to signal processing and machine learning techniques and extract a number of singing voice-related features such as vibrato and harmonic-to-noise ratio. We also propose the use of vocal segment detection and singing voice separation as preprocessing steps. Our evaluation identifies the relevant acoustic features and validates the importance of these preprocessing steps. The accuracy in timbre classification reaches 79.84% in a five-fold stratified cross validation.