Music Theory, the Missing Link Between Music-Related Big Data and Artificial Intelligence
Jeffrey A. T. Lupker, William J. Turkel · Digital humanities quarterly · 2021
This paper examines musical artificial intelligence (AI) algorithms that can not only learn from big data, but learn in ways that would be familiar to a musician or music theorist. This paper aims to find more effective links between music-related big data and artificial intelligence algorithms by incorporating principles with a strong grounding in music theory. We show that it is possible to increase the accuracy of two common algorithms (mode prediction and key prediction) by using music-theory based techniques during the data preparation process. We offer methods to alter often-used Krumhansl Kessler profiles , and the manner in which they are employed during preprocessing, to aid the connection of musical big data and mode or key predicting algorithms.