Speed-Up of Machine Learning for Sound Localization via High-Performance Computing

Abstract

Sound localization is the ability of humans to determine the source direction of sounds that they hear. Emulating this capability in virtual environments can have various societally relevant applications enabling more realistic virtual acoustics. We use a variety of artificial intelligence methods, such as machine learning via an Artificial Neural Network (ANN) model, to emulate human sound localization abilities. This paper addresses the particular challenge that the training and optimization of these models is very computationally-intensive when working with audio signal datasets. It describes the successful porting of our novel ANN model code for sound localization from limiting serial CPU-based systems to powerful, cutting-edge High-Performance Computing (HPC) resources to obtain significant speed-ups of the training and optimization process. Selected details of the code refactoring and HPC porting are described, such as adapting hyperparameter optimization algorithms to efficiently use the available HPC resources and replacing third-party libraries responsible for audio signal analysis and linear algebra. This study demonstrates that using innovative HPC systems at the Jülich Supercomputing Centre, equipped with high-tech Graphics Processing Unit (GPU) resources and based on the Modular Supercomputing Architecture, enables significant speed-ups and reduces the time-to-solution for sound localization from three days to three hours per ANN model.