  In this article, we investigate the notion of model-based deep learning in
the realm of music information research (MIR). Loosely speaking, we refer to
the term model-based deep learning for approaches that combine traditional
knowledge-based methods with data-driven techniques, especially those based on
deep learning, within a diff erentiable computing framework. In music, prior
knowledge for instance related to sound production, music perception or music
composition theory can be incorporated into the design of neural networks and
associated loss functions. We outline three specifi c scenarios to illustrate
the application of model-based deep learning in MIR, demonstrating the
implementation of such concepts and their potential.
