CAL500

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/