Adaptive Hidden Markov Noise Modelling for Speech Enhancement

File Description SizeFormat 
Bai-J-2013-PhD-Thesis.pdf2.19 MBAdobe PDFDownload
Title: Adaptive Hidden Markov Noise Modelling for Speech Enhancement
Author(s): Bai, Jiongjun
Item Type: Thesis or dissertation
Abstract: A robust and reliable noise estimation algorithm is required in many speech enhancement systems. The aim of this thesis is to propose and evaluate a robust noise estimation algorithm for highly non-stationary noisy environments. In this work, we model the non-stationary noise using a set of discrete states with each state representing a distinct noise power spectrum. In this approach, the state sequence over time is conveniently represented by a Hidden Markov Model (HMM). In this thesis, we first present an online HMM re-estimation framework that models time-varying noise using a Hidden Markov Model and tracks changes in noise characteristics by a sequential model update procedure that tracks the noise characteristics during the absence of speech. In addition the algorithm will when necessary create new model states to represent novel noise spectra and will merge existing states that have similar characteristics. We then extend our work in robust noise estimation during speech activity by incorporating a speech model into our existing noise model. The noise characteristics within each state are updated based on a speech presence probability which is derived from a modified Minima controlled recursive averaging method. We have demonstrated the effectiveness of our noise HMM in tracking both stationary and highly non-stationary noise, and shown that it gives improved performance over other conventional noise estimation methods when it is incorporated into a standard speech enhancement algorithm.
Publication Date: 2012
Date Awarded: May-2013
URI: http://hdl.handle.net/10044/1/11158
Advisor: Brookes, Mike
Department: Electrical and Electronic Engineering
Publisher: Imperial College London
Qualification Level: Doctoral
Qualification Name: Doctor of Philosophy (PhD)
Appears in Collections:Electrical and Electronic Engineering PhD theses



Items in Spiral are protected by copyright, with all rights reserved, unless otherwise indicated.

Creative Commons