Western University Scholarship@Western Electronic Thesis and Dissertation Repository 12-10-2018 10:30 AM Objective Assessment of Machine Learning Algorithms for Speech Enhancement in Hearing Aids Krishnan Parameswaran The University of Western Ontario Supervisor Parsa Vijay The University of Western Ontario Graduate Program in Electrical and Computer Engineering A thesis submitted in partial fulfillment of the equirr ements for the degree in Master of Engineering Science © Krishnan Parameswaran 2018 Follow this and additional works at: https://ir.lib.uwo.ca/etd Part of the Signal Processing Commons Recommended Citation Parameswaran, Krishnan, "Objective Assessment of Machine Learning Algorithms for Speech Enhancement in Hearing Aids" (2018). Electronic Thesis and Dissertation Repository. 5904. https://ir.lib.uwo.ca/etd/5904 This Dissertation/Thesis is brought to you for free and open access by Scholarship@Western. It has been accepted for inclusion in Electronic Thesis and Dissertation Repository by an authorized administrator of Scholarship@Western. For more information, please contact
[email protected]. Abstract 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 dereverberation for assisting hearing-impaired listeners.