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Through traditional AMDF algorithm is easy to realize, it can easily make the estimated pitch doubled. Moreover, because of the pretreatments of making frames, the algorithm’s operation speed is limited. For higher accuracy and better real-time characteristics, based on the analysis of the dynamic change law of adjacent pitch periods, this paper proposes an adaptive pitch estimation algorithm based on AMDF. By calculating the average magnitude difference function based on adaptive search range, the new approach can easily estimate the continuous speech signals without flames. So the process of pitch detection is well simplified and the precision of estimated values is improved. Our simulation results verify that the algorithm presented outperforms three well-known pitch detection methods including the AMDF algorithm, the C-AMDF algorithm and the adaptive LV-AMDF algorithm in different SNR environments.

This paper analyzes the distortion of enhanced speech based on auditory perceptual properties. A speech enhancement criteria in the sense of minimum perceptual distortion called audible distortion criteria (ADC) is proposed, the gain function of short-time spectral amplitude is deduced then. Through rough estimation for the short-time power spectra and calculation of ADC gain function, a novel speech enhancement method is given. The proposed method can make a good compromise between reinforcing the noise suppression and reducing the speech distortion. It has lower computation complexity and is simple in implementation.

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.