A Robust Noise Spectral Estimation Algorithm for Speech Enhancement in Voice Devices

In this thesis, a new robust noise spectral estimation algorithm is proposed for the purpose of single-microphone speech enhancement. This algorithm can generate the optimal noise spectral estimates in the Minimum Mean Square Error (MMSE) sense based on the speech statistics in the noisy environments. Compared to the well-adopted conventional noise spectral estimation method using the single-pole recursion, our proposed scheme is more reliable since the recursion coefficients are adaptable and optimal in the MMSE therein. We also propose a new accurate Resulting Signal-to-Noise Ratio (R-SNR) estimator as a quality measure to benchmark the existing noise spectral estimation techniques. This new R-SNR estimator can be applied to quantify not only the residual noise but also the speech distortion and therefore it can well serve as the overall speech quality measure after the noise suppression. We conduct the experiments to evaluate the performance of the noise suppression using our robust noise spectral estimation algorithm and compare it with those of two major existing noise spectral estimation methods. Through numerous simulations, we have shown that our noise suppression technique significantly outperforms the conventional methods in both stationary and nonstationary noise environments

Adaptive Hidden Markov Noise Modelling for Speech Enhancement

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

Local polynomial modeling and variable bandwidth selection for time-varying linear systems

This paper proposes a local polynomial modeling (LPM) approach and variable bandwidth selection (VBS) algorithm for identifying time-varying linear systems (TVLSs). The proposed method models the time-varying coefficients of a TVLS locally by polynomials, which can be estimated by least squares estimation with a kernel having a certain bandwidth. The asymptotic behavior of the proposed LPM estimator is studied, and the existence of an optimal local bandwidth which minimizes the local mean-square error is established. A new data-driven VBS algorithm is then proposed to estimate this optimal variable bandwidth adaptively and locally. An individual bandwidth is assigned for each coefficient instead of the whole coefficient vector so as to improve the accuracy in fast-varying systems encountered in fault detection and other applications. Important practical issues such as online implementation are also discussed. Simulation results show that the LPM-VBS method outperforms conventional TVLS identification methods, such as the recursive least squares algorithm and generalized random walk Kalman filter/smoother, in a wide variety of testing conditions, in particular, at moderate to high signal-to-noise ratio. Using local linearization, the LPM method is further extended to identify time-varying systems with mild nonlinearities. Simulation results show that the proposed LPM-VBS method can achieve a satisfactory performance for mildly nonlinear systems based on appropriate linearization. Finally, the proposed method is applied to a practical problem of voltage-flicker-tracking problem in power systems. The usefulness of the proposed approach is demonstrated by its improved performance over other conventional methods. © 2006 IEEE.published_or_final_versio