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The theory of sampling method for measuring the mean parameters of quasi-periodic signal is analyzed, and the condition without introducing measurement errors is given. The reason of measuring error caused by non-synchronous sampling and the mechanism of window weighted method to suppress measurement error are analyzed. In particular, the analytical formulas of rectangular convolution window and measurement errors are given. Aiming at the DC component in the input signal of the practical analog-to-digital conversion (ADC) circuit, a series of formulas for parameter measurement of quasi-periodic signal in engineering implementation are derived.

On the basis of the actions of a signal upon an ideal oscillator and an oscillator-array, the instantaneous energy density spectrum of a signal is defined. Furthermore, definitions of the instantaneous energy, the power density spectrum, the power, the total energy density spectrum and the total energy of a signal are introduced. Properties of power density spectrum and the relations between the power density spectrum and other physical quantities are presented. To meet the needs of real applications, an improvement method to the power density spectrum has also been mentioned.

Most algorithms for speech enhancement in the spectral domain focus on the acquisition of an estimator of the clean speech parameter, such as spectrum or amplitude. Enhanced speech quality with these methods mainly depends upon the accuracy of the estimator. When the signal-to-noise ratio (SNR) becomes lower, e.g., less than 3 dB, the enhanced speech often shows unsatisfactory quality. Here, we propose a new method for speech enhancement in the spectral domain. It is based on our view that speech characteristics are perceived mainly by part of the spectral components which have higher local (or instantaneous) SNRs. These components have a special importance on speech SNR enhancement and speech understanding. Identifying these components in the noisy spectrum with a decision system and constructing enhanced speech just by the same noisy amplitude and phase, the result is similar to those popular algorithms. All that is needed is a local SNR decision for each spectral component. The threshold for the decision is derived and two popular methods for local SNR (LSNR) estimation are suggested. It shows better performance under lower SNR signals. Though the number of these kind of spectral components varies with different signal SNR, any algorithms which can pick up more than 90% of it is sufficient to match any existing methods. Primary considerations and results are shown.