Algorithm and architecture for simultaneous diagonalization of matrices applied to subspace-based speech enhancement

This thesis presents algorithm and architecture for simultaneous diagonalization of matrices. As an example, a subspace-based speech enhancement problem is considered, where in the covariance matrices of the speech and noise are diagonalized simultaneously. In order to compare the system performance of the proposed algorithm, objective measurements of speech enhancement is shown in terms of the signal to noise ratio and mean bark spectral distortion at various noise levels. In addition, an innovative subband analysis technique for subspace-based time-domain constrained speech enhancement technique is proposed. The proposed technique analyses the signal in its subbands to build accurate estimates of the covariance matrices of speech and noise, exploiting the inherent low varying characteristics of speech and noise signals in narrow bands. The subband approach also decreases the computation time by reducing the order of the matrices to be simultaneously diagonalized. Simulation results indicate that the proposed technique performs well under extreme low signal-to-noise-ratio conditions. Further, an architecture is proposed to implement the simultaneous diagonalization scheme. The architecture is implemented on an FPGA primarily to compare the performance measures on hardware and the feasibility of the speech enhancement algorithm in terms of resource utilization, throughput, etc. A Xilinx FPGA is targeted for implementation. FPGA resource utilization re-enforces on the practicability of the design. Also a projection of the design feasibility for an ASIC implementation in terms of transistor count only is include

OBJECTIVE AND SUBJECTIVE EVALUATION OF DEREVERBERATION ALGORITHMS

Reverberation significantly impacts the quality and intelligibility of speech. Several dereverberation algorithms have been proposed in the literature to combat this problem. A majority of these algorithms utilize a single channel and are developed for monaural applications, and as such do not preserve the cues necessary for sound localization. This thesis describes a blind two-channel dereverberation technique that improves the quality of speech corrupted by reverberation while preserving cues that affect localization. The method is based by combining a short term (2ms) and long term (20ms) weighting function of the linear prediction (LP) residual of the input signal. The developed and other dereverberation algorithms are evaluated objectively and subjectively in terms of sound quality and localization accuracy. The binaural adaptation provides a significant increase in sound quality while removing the loss in localization ability found in the bilateral implementation

Sleep Stage Classification: A Deep Learning Approach

Sleep occupies significant part of human life. The diagnoses of sleep related disorders are of great importance. To record specific physical and electrical activities of the brain and body, a multi-parameter test, called polysomnography (PSG), is normally used. The visual process of sleep stage classification is time consuming, subjective and costly. To improve the accuracy and efficiency of the sleep stage classification, automatic classification algorithms were developed. In this research work, we focused on pre-processing (filtering boundaries and de-noising algorithms) and classification steps of automatic sleep stage classification. The main motivation for this work was to develop a pre-processing and classification framework to clean the input EEG signal without manipulating the original data thus enhancing the learning stage of deep learning classifiers. For pre-processing EEG signals, a lossless adaptive artefact removal method was proposed. Rather than other works that used artificial noise, we used real EEG data contaminated with EOG and EMG for evaluating the proposed method. The proposed adaptive algorithm led to a significant enhancement in the overall classification accuracy. In the classification area, we evaluated the performance of the most common sleep stage classifiers using a comprehensive set of features extracted from PSG signals. Considering the challenges and limitations of conventional methods, we proposed two deep learning-based methods for classification of sleep stages based on Stacked Sparse AutoEncoder (SSAE) and Convolutional Neural Network (CNN). The proposed methods performed more efficiently by eliminating the need for conventional feature selection and feature extraction steps respectively. Moreover, although our systems were trained with lower number of samples compared to the similar studies, they were able to achieve state of art accuracy and higher overall sensitivity

Entropy Theory for Streamflow Forecasting

Entropy spectral analysis is developed for monthly streamflow forecasting, which contains the use of configurational entropy and relative entropy. Multi-channel entropy spectral analysis is developed for long-term drought forecasting with climate indicators. The configurational entropy spectral analysis (CESA) is developed with both spectral power and frequency as random variables. With spectral power as a random variable, the configurational entropy spectral analysis (CESAS) identical to the original Burg entropy spectral analysis (BESA) when the underlying process is Gaussian. Through examination using monthly streamflow from the Mississippi Watershed, CESAS and BESA yield the same results and two methods are considered equivalent or as one method. With frequency as a random variable, the configurational entropy spectral analysis (CESAF) is developed and tested using monthly streamflow data from 19 river basins covering a broad range of physiographic characteristics. Testing shows that CESAF captures streamflow seasonality and satisfactorily forecasts both high and low flows. When relative drainage area is considered for analyzing streamflow characteristics and spectral patterns, it is found that upstream streamflow is forecasted more accurately than downstream streamflow. Minimum relative entropy spectral analysis (MRESA) is developed under two conditions: spectral power as a random variable (RESAS) and frequency as a random variable (RESAF). The exponential distribution was chosen as a prior probability in the RESAS theory, and in the RESAF theory, the prior is chosen from the periodicity of streamflow. Both MRESA theories were evaluated using monthly streamflow observed at 20 stations in the Mississippi River basin, where forecasted monthly streamflow shows higher reliability in the Upper Mississippi than in the Lower Mississippi. The proposed univariate entropy spectral analyses are generally recommended over the classical autoregressive (AR) process for higher reliability and longer forecasting lead time. By comparing two MRESA theories with the two maximum entropy spectral analyses (MESA) (BESA and CESA), it is found that MRESA provided higher resolution in spectral estimation and more reliable streamflow forecasting, especially for multi-peak flow conditions. The MRESA theory is more accurate in forecasting streamflow for both peak and low flow values with longer lead time than MESA. Besides, choosing frequency as a random variable shows advantages over choosing spectral power. Spectral density estimated by the RESAF or CESAF theory shows higher resolution than the RESAS or BESA theory, respectively, and streamflow forecasted by RESAF or CESAF is more reliable than that by RESAS or BESA, respectively. Finally, multi-channel entropy spectral analysis (MCESA) is developed for bivariate or multi-variate time series forecasting. MCESA theory is verified by forecasting long-term standardized streamflow index with El Nino Southern Oscillation (ENSO) indicator. SSI was successfully forecasted using multi-channel spectral analysis with ENSO as an indicator. The monthly drought series is forecasted for lead times of 4-6 years by MCESA