Objective Assessment Of Machine Learning Algorithms For Speech Enhancement In Hearing Aids, Krishnan Parameswaran

Electronic Thesis and Dissertation Repository

Speech enhancement in assistive hearing devices has been an area of research for many decades. Noise reduction is particularly challenging because of the wide variety of noise sources and the non-stationarity of speech and noise. Digital signal processing (DSP) algorithms deployed in modern hearing aids for noise reduction rely on certain assumptions on the statistical properties of undesired signals. This could be disadvantageous in accurate estimation of different noise types, which subsequently leads to suboptimal noise reduction. In this research, a relatively unexplored technique based on deep learning, i.e. Recurrent Neural Network (RNN), is used to perform noise reduction and …

Development And Evaluation Of A Real-Time Framework For A Portable Assistive Hearing Device, Aleksandar Dimitrov Mihaylov

Electronic Thesis and Dissertation Repository

Testing and verification of digital hearing aid devices, and the embedded software and algorithms can prove to be a challenging task especially taking into account time-to-market considerations. This thesis describes a PC based, real-time, highly configurable framework for the evaluation of audio algorithms. Implementation of audio processing algorithms on such a platform can provide hearing aid designers and manufacturers the ability to test new and existing processing techniques and collect data about their performance in real-life situations, and without the need to develop a prototype device. The platform is based on the Eurotech Catalyst development kit and the Fedora Linux …