CAL500 (Computer Audition Lab 500)

Introduced by Douglas Turnbull et al. in Semantic Annotation and Retrieval of Music and Sound Effects

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.").


Paper Code Results Date Stars

Dataset Loaders

No data loaders found. You can submit your data loader here.


Similar Datasets