Spatial audio has been studied for several decades, but has seen much renewed interest
recently due to advances in both software and hardware for capture and playback, and the …

The domain of spatial audio comprises methods for capturing, processing, and reproducing
audio content that contains spatial information. Data-based methods are those that operate …

Recently deep learning and machine learning approaches have been widely employed for
various applications in acoustics. Nonetheless, in the area of sound field processing and …

Recent developments in acoustic signal processing have seen the integration of deep
learning methodologies, alongside the continued prominence of classical wave expansion …

This article proposes a system for object-based six-degrees-of-freedom (6DoF) rendering of
spatial sound scenes that are captured using a distributed arrangement of multiple …

Non-interactive and linear experienceslike cinema film offer high quality surround sound
audio to enhance immersion, however, the perspective is usually fixed to the recording …

Most often, virtual acoustic rendering employs real-time updated room acoustic simulations
to accomplish auralization for a variable listener perspective. As an alternative, we propose …

State-of-the-art soundfield reconstruction methods are computationally expensive and their
performance is generally undermined by the presence of strong reverberation and of near …

First-order Ambisonics (FOA) recordings can be processed and reproduced over
headphones. They can be rotated to account for the listener's head orientation. However …

In this paper, we propose a data-driven approach for the reconstruction of unknown room
impulse responses (RIRs) based on the deep prior paradigm. We formulate RIR …