In the field of music computing, many methods aim at automatically extracting musical information such as beat times, chords, and keys from raw audio data.
Publishing the automatically extracted data can be useful in several ways: researchers working on similar methods can compare their results with those published; non-specialist observers can examine the state of the art; the published data can be used as input for new research methods (e.g. for music classification).
Automatic chord and key transcriptions from the OMRAS2 project of the songs in the 2009 MIREX chord detection data set
These 210 songs, mainly rock and pop music by Queen, The Beatles, and Zweieck, are a subset of the larger OMRAS2 Metadata Project corpus, which is also published on this site.
Chords and keys are annotated in the WaveSurfer .lab format, with chords labelled according to Chris Harte's syntax. 
Inference is done by the method described in , with parameter setting std-0.6: bass and treble chromagrams are calculated from the wave form of a song via an approximate transcription that uses the NNLS algorithm . These chromagrams are then used as an input to a musically motivated dynamic Bayesian network, which models metric position, key, chord, bass note as hidden layers, and the two chroma representations as observed layers. The automatic transcriptions of chords and keys are taken from the inferred most likely explanation of the chromagrams in terms of the hidden states (in the Viterbi sense) of the dynamic Bayesian network.
 C. Harte, M. Sandler, S. A. Abdallah, and E. Gómez. Symbolic representation of musical chords: A proposed syntax for text annotations. In Proceedings of the 6th International Conference on Music Informa- tion Retrieval, ISMIR 2005, London, UK, pages 66–71, 2005.
 Matthias Mauch and Simon Dixon, Approximate Note Transcription for the Improved Identification of Difficult Chords, to appear in the Proceedings of the 11th International Society for Music Information Retrieval Conference (ISMIR 2010), 2010
 C. L. Lawson and R. J. Hanson. Solving Least Squares Problems, chapter 23. Prentice-Hall, 1974.