Computational musicology

Computational musicology is defined as the study of music with computational modelling and simulation. It saw its beginning in the 1950s and originally did not use computers, but more of statistical and mathematical methods. Nowadays computational musicology depends mostly on complex algorithms to either go through vast amounts of information or produce music using given parameters. Several alternative names and subdisciplines of the field include mathematical music theory, computer music, systematic musicology, music information retrieval, computational musicology, digital musicology, sound and music computing and music informatics.

Lejaren Hiller acted as one of the foremost pioneers by creating one of the first musical compositions with a computer in 1957. In the 1960s research continued using statistical and mathematical methods, and started to use computers in an increasing manner as their capabilities grew. 1970s and 1980s were especially significant times for computational musicology as many discoveries were made. Since then the field has suffered a general lack of interest.

Most of the work in computational musicology is done with computers that run specifically designed programs. Commonly they employ the theory and methods statistical science, mathematics and music theory. Comprehension of the physics of hearing and sound are also required in analysis of raw audio data.

One of the earliest applications in computational musicology was the creation and use of musical databases. Input, usage and analysis of large amounts of data can be very troublesome using manual methods while usage of computers can make such tasks considerably easier.

Different computer programs have been developed to analyze musical data. Data formats vary from standard notation to raw audio. Analysis of formats that are based on storing all properties of each note, for example MIDI, were used originally and are still among the most common methods. Significant advances in analysis of raw audio data have been made only recently.

Different algorithms can be used to both create complete compositions and improvise music. One of the methods by which a program can learn improvisation is analysis of choices a human player makes while improvising. Artificial neural networks are used extensively in such applications.

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