  For the tasks of automatic music emotion recognition, genre recognition,
music recommendation it is helpful to be able to extract mode from any section
of a musical piece as a perceived amount of major or minor mode (majorness)
inside that section, perceived as a whole (one or several melodies and any
harmony present). In this paper we take a data-driven approach (modeling
directly from data without giving an explicit definition or explicitly
programming an algorithm) towards modeling this property. We collect
annotations from musicians and show that majorness can be understood by
musicians in an intuitive way. We model this property from the data using deep
learning.
