Exploring Audio Interfaces for Vertical Guidance in Augmented Reality via Hand-Based Feedback.

This research proposes an evaluation of pitch-based sonification methods via user experiments in real-life scenarios, specifically vertical guidance, with the aim of standardizing the use of audio interfaces in AR in guidance tasks. Using literature on assistive technology for people who are blind or visually impaired, we aim to generalize their applicability to a broader population and for different use cases. We propose and test sonification methods for vertical guidance in a series of hand-navigation assessments with users without visual feedback.

Mel frequency cepstral coefficient temporal feature integration for classifying squeak and rattle noise.

Fault identification using the emitted mechanical noise is becoming an attractive field of research in a variety of industries. It is essential to rank acoustic feature integration functions on their efficiency to classify different types of sound for conducting a fault diagnosis. The Mel frequency cepstral coefficient (MFCC) method was used to obtain various acoustic feature sets in the current study.

Utilising reinforcement learning to develop strategies for driving auditory neural implants.

Objective: In this paper we propose a novel application of reinforcement learning to the area of auditory neural stimulation. We aim to develop a simulation environment which is based off real neurological responses to auditory and electrical stimulation in the cochlear nucleus (CN) and inferior colliculus (IC) of an animal model. Using this simulator we implement closed loop reinforcement learning algorithms to determine which methods are most effective at learning effective acoustic neural stimulation strategies.