Digital Signal Processing Methodologies for Audio System Modeling and Audio Output Enhancement

This chapter summarizes some of the main results I obtained during my Ph.D. studies at Politecnico di Milano, under the supervision of Professor Augusto Sarti and Professor Alberto Bernardini. Audio systems have become, nowadays, pervasive in many different market sectors, such as that of consumer electronics and biomedical devices. To accurately represent, digitally replicate, and process the signals of such complex systems, it is crucial to develop multiphysics models that capture their nonlinear behaviors. In this chapter, I introduce novel multiphysics models of audio systems together with innovative digital signal processing techniques with the ultimate goal of improving their acoustic response. By leveraging the efficiency and accuracy of Wave Digital Filters, I propose novel modeling methods to incorporate the various physical domains involved in audio systems. I introduce iterative methods for streamlined emulation and parallel implementation, addressing, at the same time, complex nonlinearities such as magnetic saturation and hysteresis. I present new linearization and virtualization algorithms to manipulate the audio device behavior leveraging on the newly introduced multiphysics models. Finally, I combine psychoacoustic methodologies with deep-learning models to tackle operating conditions affected by very strict physical constraints. With this investigation, I cover diverse audio signal processing tasks, offering fresh insights and practical solutions across various application scenarios.