Today, computational musicology encompasses a wide range of research topics dealing with the multiple ways music can be represented.
Generally, the field is considered to be an extension of a much longer history of intellectual inquiry in music that overlaps with science, mathematics, technology,[5] and archiving.
[7] Other work at this time at Princeton University chiefly driven by Arthur Mendel, and implemented by Michael Kassler[8] and Eric Regener helped push forward the Intermediary Musical Language (IML) and Music Information Retrieval (MIR) languages that later fell out of popularity in the late 1970s.
The 1960s also marked a time of documenting bibliographic initiatives such as the Repertoire International de Literature Musicale (RILM) created by Barry Brook in 1967.
[3] This task driven motivation lead to the development of MUSTRAN for music analysis by led by Jerome Wenker and Dorothy Gross at Indiana University.
John Walter Hill began developing a commercial program called Savy PC that was meant to help musicologists analyze lyrical content in music.
[9] Using software developed at the time, Sandra Pinegar examined 13th century music theory manuscripts in her doctoral work at Columbia University in order to gain evidence on the dating and authoring of texts.
Lastly, audio data refers to recording of the representations of the acoustic wave or sound that results from changes in the oscillations of air pressure.
For example, the notation of Hindustani ragas will begin with an alap that does not demand a strict adherence to a beat or pulse, but is left up to the discretion of the performer.
[4] Two of the most common software choices for analyzing symbolic data are David Huron's Humdrum Toolkit[14] and Michael Scott Cuthbert's music21.
One developing sociomusicological theory in computational musicology is the "Discursive Hypothesis" proposed by Kristoffer Jensen and David G. Hebert, which suggests that "because both music and language are cultural discourses (which may reflect social reality in similarly limited ways), a relationship may be identifiable between the trajectories of significant features of musical sound and linguistic discourse regarding social data.
"[17] According to this perspective, analyses of "big data" may improve our understandings of how particular features of music and society are interrelated and change similarly across time, as significant correlations are increasingly identified within the musico-linguistic spectrum of human auditory communication.
For example, professors affiliated with the Birla Institute of Technology in India have produced studies of harmonic and melodic tendencies (in the raga structure) of Hindustani classical music.