Pitch estimation, voicing decision, and noise spectrum estimation for speech corrupted by high levels of additive noise

This dissertation presents two algorithms that extract parameters which are important to speech processing in high levels of noise. The first algorithm determines whether a signal containing noise corrupted human speech is voiced or not and estimates the fundamental frequency (pitch) of voiced speech. The second algorithm produces an estimate of the additive noise which is corrupting the speech. Previous research related to the voicing decision and pitch estimation has been concentrated at signal-to-noise ratios (SNRs) above 0 dB. Consequently, speech processing requiring the extraction of these parameters in higher levels of noise could not be performed with much success. The research presented in this dissertation concentrates on SNRs around and below 0 dB. Although the algorithm, based on the autocorrelation function, is designed to work well for high levels of noise, good results for the no noise case have been maintained. The idea of a confidence measure for parameter estimation is introduced. Confidence measures are defined and developed for both the voicing decision and the pitch estimation algorithms. Estimation of noise that is corrupting a speech signal has been motivated by the need to enhance the corrupted speech. Previous research has concentrated on speech which is band limited to about 3500 Hz. Therefore, the estimation of the noise corrupting high frequency speech had not been considered. The noise estimation algorithm presented in this dissertation considers the effects of high frequency speech on the noise estimate in addition to the effects of low frequency speech. A new spectral averaging method is introduced which significantly reduces the corrupting effect of the speech components on the noise estimate for SNRs above 0 dB. The algorithm is tested for stationary white noise, stationary non-white noise, and non-stationary white noise

The effect of breathing 100 percent oxygen on short-term memory of military officers while under heat stress

Using a serial short term memory task, subjects were required to respond to symbols presented one-back, two-back, and three-back from a randomly presented list of four different symbols while breathing either 100 percent oxygen or atmospheric air with an oxygen mask in a heat stressful environment. The purpose of the experiment was to determine if breathing 100 percent oxygen had any effect on the short term memory of a subject under heat stress. Analysis of the data collected from 10 subjects under heat stress indicated breathing pure oxygen had no effect in the 15 minute period on short term memory.http://archive.org/details/effectofbreating00krubLieutenant, United States NavyApproved for public release; distribution is unlimited

Comparison of Pitch Tracking Methods for Speech in Additive White Gaussian Noise

An exploration of five methods (and variations of these methods) for the estimation of the fundamental frequency of a voiced speech signal corrupted by high levels of additive white Gaussian noise is discussed. The time domain methods explored include the autocorrelation and the cepstrum. The frequency domain methods explored include the evaluation of the magnitude Fourier transform, the autocorrelation of the magnitude Fourier transform, and the harmonic sum spectrum. All methods are evaluated on a fixed window basis and quantitative results are presented for seven signal-to-noise ratios. In addition, this thesis includes a discussion of several issues related to pitch tracking as well as some general conclusions regarding the pitch tracking methods explored