Content-Based Audio Classification and Retrieval for Audiovisual Data Parsing
Content-Based Audio Classification and Retrieval for Audiovisual Data Parsing
Automatic singer identification
ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 2
Exploring Vibrato-Motivated Acoustic Features for Singer Identification
IEEE Transactions on Audio, Speech, and Language Processing
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Timbre can be defined as feature of an auditory stimulus that allows us to distinguish the sounds which have the same pitch and loudness. In this paper, we explore timbre based perceptual feature for singer identification. We start with a vocal detection process to extract the vocal segments from the sound. The cepstral coefficients, which reflect timbre characteristics, are then computed from the vocal segments. The cepstral coefficients of timbre are formulated by combining information of harmonic and the dynamic characteristics of the sound such as vibrato and the attack-decay envelope of the songs. Bandpass filters that spread according to the octave frequency scale are used to extract vibrato and harmonic information of sounds. The experiments are conducted on a database of 84 popular songs. The results show that the proposed timbre based perceptual feature is robust and effective. We achieve an average error rate of 12.2% in segment level singer identification.