Studies in Signal Processing Techniques for Speech Enhancement: A comparative study

Speech enhancement is very essential to suppress the background noise and to increase speech intelligibility and reduce fatigue in hearing. There exist many simple speech enhancement algorithms like spectral subtraction to complex algorithms like Bayesian Magnitude estimators based on Minimum Mean Square Error (MMSE) and its variants. A continuous research is going and new algorithms are emerging to enhance speech signal recorded in the background of environment such as industries, vehicles and aircraft cockpit. In aviation industries speech enhancement plays a vital role to bring crucial information from pilot’s conversation in case of an incident or accident by suppressing engine and other cockpit instrument noises. In this work proposed is a new approach to speech enhancement making use harmonic wavelet transform and Bayesian estimators. The performance indicators, SNR and listening confirms to the fact that newly modified algorithms using harmonic wavelet transform indeed show better results than currently existing methods. Further, the Harmonic Wavelet Transform is computationally efficient and simple to implement due to its inbuilt decimation-interpolation operations compared to those of filter-bank approach to realize sub-bands

Active Noise Control using Variable step-size Griffiths’ LMS (VGLMS) algorithm on Real-Time platform

This paper proposes implementation of Griffith’s Variable step-size algorithm for Active Noise Control (ANC) on ADSP-TS201 EZ-Kit Lite. The dual computational units and execution of up to four instructions per cycle which are special features over other processors are best utilized to generate an optimized code. The VGLMS provides improved secondary path estimation and computations involved are marginal as the same gradient is used for step-size computation and coefficient adaptation. The improved secondary path estimate, in turn improves the ANC performance. Further, variable step-size algorithm is used for the main-path to achieve faster convergence. Both for narrowband (fundamental and its harmonics) and broadband noise fields, for a duct the attenuation achieved is 25 dB and 15 dB respectively. The program execution time was only 1.25% for an input sampling rate of 1 KHz which indicates the utility of the special features of the processor considered. Further these features have enabled in bringing down the program memory requirement in the implementation of the algorithm

A generalised porous medium approach to study thermo-fluid dynamics in human eyes

The present work describes the application of the generalised porous medium model to study heat and fluid flow in healthy and glaucomatous eyes of different subject specimens, considering the presence of ocular cavities and porous tissues. The 2D computational model, implemented into the open-source software OpenFOAM, has been verified against benchmark data for mixed convection in domains partially filled with a porous medium. The verified model has been employed to simulate the thermo-fluid dynamic phenomena occurring in the anterior section of four patient-specific human eyes, considering the presence of anterior chamber (AC), trabecular meshwork (TM), Schlemm’s canal (SC), and collector channels (CC). The computational domains of the eye are extracted from tomographic images. The dependence of TM porosity and permeability on intraocular pressure (IOP) has been analysed in detail, and the differences between healthy and glaucomatous eye conditions have been highlighted, proving that the different physiological conditions of patients have a significant influence on the thermo-fluid dynamic phenomena. The influence of different eye positions (supine and standing) on thermo-fluid dynamic variables has been also investigated: results are presented in terms of velocity, pressure, temperature, friction coefficient and local Nusselt number. The results clearly indicate that porosity and permeability of TM are two important parameters that affect eye pressure distribution

Improved system blind identification based on second-order13; cyclostationary statistics: A group delay approach

An improved system blind identification method based on secondorder cyclostationary statistics and the properties of group delay, has been proposed. This is achieved by applying a correction to the estimated phase (by13; the spectral correlation density of the system output) for the poles, in the group delay domain. The results indicate a significant improvement in system blind13; identification, in terms of root mean square error. Depending upon the signalto-noise ratio, the improvement in percentage normalized mean square error13; ranges between 20 and 50%

Autoregressive modeling of the Wigner-Ville distribution based on signal decomposition and modified group delay

An autoregressive modeling of the Wigner-Ville distribution (WVD), based on signal decomposition (SD) by a perfect reconstruction filter bank (PRFB) and the modified magnitude group delay function (MMGD), has been proposed. The SD and MMGD, respectively, reduce the existence of crossterms (without any time smoothing) and the Gibb's ripple effect (due to truncation of the WVD kernel, without applying any window), significantly. In view of this, the modeling is not affected by either the crossterms or the Gibb's ripple and the window that would have been used. The proposed method represents actual time-frequency information parsimoniously and compared to the existing WVD modeling methods, its performance is significantly better in terms of both time and frequency resolution (as there is no time and frequency smoothing) and noise immunity/variance and is computationally efficient. xA9; 2003 Elsevier B.V. All rights reserved

Improved phase estimation based on complete bispectrum and modified group delay

In this paper, a new method for extracting the system phase from the bispectrum13; of the system output has been proposed. This is based on the complete bispectral data13; computed in the frequency domain and modified group delay. The frequency domain13; bispectrum computation improves the frequency resolution and the modified group delay13; reduces the variance preserving the frequency resolution. The use of full bispectral data13; also reduces the variance as it is used for averaging. For the proposed method at a signal13; to noise ratio of 5dB, the reduction in root mean square error is in the range of 1.5 to 713; times over the other methods considered

Discrete cosine harmonic wavelet transform and its application to signal compression and subband spectral estimation using modified group delay

This paper proposes a new harmonic wavelet transform (HWT) based on13; Discrete Cosine Transform (DCTHWT) and its application for signal or image13; compression and subband spectral estimation using Modified Group Delay (MGD).13; Further, the existing DFTHWT has also been explored for image compression. The13; DCTHWT provides better quality decomposed decimated signals, which enable13; improved compression and MGD processing. For signal/image compression, compared13; to the HWT based on DFT (DFTHWT), the DCTHWT reduces the reconstruction error.13; Compared to DFTHWT for the speech signal considered for a compression factor of13; 0.62, the DCTWHT provides a 30% reduction in reconstruction error. For an image, the13; DCTHWT algorithm due to its real nature, is computationally simple and more accurate13; than the DFTHWT. Further compared to Cohen-Daubechies-Feauveau 9/7 biorthogonal13; symmetric wavelet, the DCTHWT, with its computational advantage, gives a better or13; comparable performance. For an image with 6.25% coefficients, the reconstructed13; image by DFTHWT is significantly inferior in appearance to that by DCTHWT which13; is reflected in the error index as its values are 3.0% and 2.65% respectively.13; For spectral estimation, DCTHWT reduces the bias both in frequency13; (frequency resolution) and spectral magnitude. The reduction in magnitude bias in turn13; improves the signal detectability. In DCTHWT, the improvement in frequency13; resolution and the signal detectability is not only due to good quality DCT subband13; signals but also due to their stretching (decimation) in the wavelet transform. The MGD13; reduces the variance while preserving the frequency resolution achieved by DCT and13; decimation. In view of these, the new spectral estimator facilitates a significant13; improvement both in magnitude and frequency bias, variance and signal detection13; ability; compared to those of MGD processing of both DFT and DCT fullband and DFT13; subband signal

Improved instantaneous power spectrum (IPS) performance:13; A group delay approach

The instantaneous power spectrum (IPS) has many advantages over the Wignerx2013;Ville distribution. However, it suffers from ringing effect and poor frequency resolution. In this paper, a new method, improved IPS (IIPS) based on modified group delay function (GDFM) has been suggested for reducing the ringing effect. In this approach, since the ringing effect has been removed without using any common window function, it provides improved frequency resolution. Further, in IIPS, the GDFM enables signal-to-noise ratio (SNR) enhancement, in the timex2013;frequency representation (TFR), as it not only reduces the effect of zeros which cause ringing but also those due to noise associated with the signal. The performance of the proposed method is illustrated by simulation for FSK and linear chirp signals and it has been observed that it improves: the frequency resolution significantly and the SNR. 13; 13

A new Delayless Subband Adaptive Filter based on Discrete Co

This paper proposes a new delayless subband adaptive filter (SAF) based on Discrete Cosine Harmonic Wavelet Transform (CHWT). The subband signals derived using CHWT are of real nature due to DCT. This enables to obtain the estimate of the overall plant impulse response in the DCT domain and hence is simple compared to that of difficult stacking used in delayless SAF realized by DFT filter banks. Further the aliasing due to filtering is absent as subband signals are derived in DCT domain and hence critical sampling is sufficient. This does not suffer from eigen value spread, due to over sampling used to avoid aliasing. The wavelet nature in addition to handling nonstationarity provides the perception. Hence, the new algorithm is simple and provides good convergence rate for signals with large eigenvalue spread and of nonstationary nature

Non-stationary signal analysis by least mean fourth (LMF) adaptive algorithm

A modified least mean fourth (LMF) adaptive algorithm applicable to non-stationary signals is presented. The performance of the proposed algorithm is studied by simulation for non-stationarities in bandwidth, centre frequency and gain of a stochastic signal. These non-stationarities are in the form of linear, sinusoidal and jump variations of the parameters. The proposed LMF adaptation is found to have better parameter tracking capability than the LMS adaptation for the same speed of convergence