CAL500

홈페이지 · 논문 21편

CAL500 (Computer Audition Lab 500) is a dataset aimed for evaluation of music information retrieval systems. It consists of 502 songs picked from western popular music. The audio is represented as a time series of the first 13 Mel-frequency cepstral coefficients (and their first and second derivatives) extracted by sliding a 12 ms half-overlapping short-time window over the waveform of each song. Each song has been annotated by at least 3 people with 135 musically-relevant concepts spanning six semantic categories: * 29 instruments were annotated as present in the song or not, * 22 vocal characteristics were annotated as relevant to the singer or not, * 36 genres, * 18 emotions were rated on a scale from one to three (e.g., `not happy", neutral", `happy"), * 15 song concepts describing the acoustic qualities of the song, artist and recording (e.g., tempo, energy, sound quality), * 15 usage terms (e.g., "I would listen to this song while driving, sleeping, etc."). Source: http://calab1.ucsd.edu/~datasets/cal500/details_cal500.txt Audio Source: http://calab1.ucsd.edu/~datasets/cal500/cal500data/