Automatic singer identification
ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 2
IEEE Transactions on Audio, Speech, and Language Processing
Singer identification using time-frequency audio feature
ISNN'11 Proceedings of the 8th international conference on Advances in neural networks - Volume Part II
Context-Aware features for singing voice detection in polyphonic music
AMR'11 Proceedings of the 9th international conference on Adaptive Multimedia Retrieval: large-scale multimedia retrieval and evaluation
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This paper describes a method of the characteristics of a singing voice from polyphonic musical audio signals including sounds of various musical instruments singing voices play an important role in musical pieces with vocals, such representation is useful for music information retrieval systems. The main problem in modeling the characteristics of a singing voice is the negative influences caused by accompaniment sounds. To solve this problem, we developed two methods accompaniment sound reduction and reliable frame selection. The former makes it possible to calculate feature vectors that represent a spectral envelope of a singing voice after reducing accompaniment sounds. It first extracts the harmonic components of the predominant melody from sound mixtures and then resynthesizes melody by using a sinusoidal model driven by these components. The latter method then estimates the reliability of frame of the obtained melody (i.e., the influence of accompaniment sound) by using two Gaussian mixture models (GMMs) for vocal and nonvocal frames to select the reliable vocal portions of musical pieces. Finally, each song is represented by its GMM consisting of the reliable frames. This new representation of the singing voice is demonstrated to improve the performance of an automatic singer identification system and to achieve an MIR system based on vocal timbre similarity.