Kepstrum approach to real-time speech-enhancement methods using two microphones

The objective of this paper is to provide improved real-time noise canceling performance by using kepstrum analysis. The method is applied to typically existing two-microphone approaches using modified adaptive noise canceling and speech beamforming methods. It will be shown that the kepstrum approach gives an improved effect for optimally enhancing a speech signal in the primary input when it is applied to the front-end of a beamformer or speech directivity system. As a result, enhanced performance in the form of an improved noise reduction ratio with highly reduced adaptive filter size can be achieved. Experiments according to 20cm broadside microphone configuration are implemented in real-time in a real environment, which is a typical indoor office with a moderate reverberation condition

Log spectral estimation for stationary and nonstationary processes

Journal ArticleThis research is concerned with two log spectral estimators in the context of both stationary and nonstationary signals. They differ because in one smoothing is realized before the logarithmic transformation, while the other is smoothed in the logarithimc domain. It is shown that for stationary signals the two estimators are similar, differing in expected value by only a universal constant. The first estimator, however, is smoother. For nonstationary signals, the estimators are biased by different amounts dependent upon the nonstationarity. The difference between the estimators is shown to be a sensitive test for nonstationarity. The estimators are used in the analysis and implementation of two solutions to the problem of blind deconvolution. It is found that the methods are equivalent for stationary signals, bu differ markedly for nonstationary signals in the presence of stationary background noise. Recommendations are made for the practical digital implementation of the practical digital implementation of the log spectral estimators

The acousto-ultrasonic approach

The nature and underlying rationale of the acousto-ultrasonic approach is reviewed, needed advanced signal analysis and evaluation methods suggested, and application potentials discussed. Acousto-ultrasonics is an NDE technique combining aspects of acoustic emission methodology with ultrasonic simulation of stress waves. This approach uses analysis of simulated stress waves for detecting and mapping variations of mechanical properties. Unlike most NDE, acousto-ultrasonics is less concerned with flaw detection than with the assessment of the collective effects of various flaws and material anomalies. Acousto-ultrasonics has been applied chiefly to laminated and filament-wound fiber reinforced composites. It has been used to assess the significant strength and toughness reducing effects that can be wrought by combinations of essentially minor flaws and diffuse flaw populations. Acousto-ultrasonics assesses integrated defect states and the resultant variations in properties such as tensile, shear, and flexural strengths and fracture resistance. Matrix cure state, porosity, fiber orientation, fiber volume fraction, fiber-matrix bonding, and interlaminar bond quality are underlying factors

GLOTTAL EXCITATION EXTRACTION OF VOICED SPEECH - JOINTLY PARAMETRIC AND NONPARAMETRIC APPROACHES

The goal of this dissertation is to develop methods to recover glottal flow pulses, which contain biometrical information about the speaker. The excitation information estimated from an observed speech utterance is modeled as the source of an inverse problem. Windowed linear prediction analysis and inverse filtering are first used to deconvolve the speech signal to obtain a rough estimate of glottal flow pulses. Linear prediction and its inverse filtering can largely eliminate the vocal-tract response which is usually modeled as infinite impulse response filter. Some remaining vocal-tract components that reside in the estimate after inverse filtering are next removed by maximum-phase and minimum-phase decomposition which is implemented by applying the complex cepstrum to the initial estimate of the glottal pulses. The additive and residual errors from inverse filtering can be suppressed by higher-order statistics which is the method used to calculate cepstrum representations. Some features directly provided by the glottal source\u27s cepstrum representation as well as fitting parameters for estimated pulses are used to form feature patterns that were applied to a minimum-distance classifier to realize a speaker identification system with very limited subjects