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In this paper, the peak finding algorithms corresponding to the Autocorrelation Function (ACF), which are widely exploited for detecting the pitch of voiced signal, are proposed.According to various researchers, it is well known fact that the estimation of fundamental frequency (F0) in speech signal is not only very important task but quite difficult mission. The proposed algorithms, presented in this paper, are implemented by using many characteristics -such as monotonic increasing function -of ACF function. Thus, the proposed algorithms may be able to estimate both reliable and correct the fundamental frequency as long as the autocorrelation function of speech signal is accurate. Since the proposed algorithms may reduce the computational complexity it can be applied to the real-time processing.

3 6Abstract Peaks (or Nulls) finding algorithms for Average Magnitude Difference Function (AMDF) of speech signal are proposed in this paper. Both AMDF and Autocorrelation Function (ACF) are widely used to estimate a pitch of speech signal. It is well known that the estimation of the fundamental requency (F0) for speech signal is not only important but also very difficult.In this paper, two algorithms, are exploited the characteristics of AMDF, are proposed. First, the proposed algorithm which has a Threshold value is applied to the local minima to detect a pitch period. The Other proposed algorithm to estimate a pitch period of speech signal is utilized the relationship between AMDF and ACF. The data in this paper, is recorded by using general commercial device, is composed of Korean emotion expression words. The recorded speech data are applied to two proposed algorithms and tested their performance.

In this paper, the novel method that infers human emotions connecting to changes in brain waves is investigated. General methods or algorithms in this field of research extract the features from brain waves and use classifiers such as Support Vector Machine (SVM) and K-Near Neighbor (KNN), based on the artificial intelligence to explain human emotions. The novel method presented in this paper is to use a data table instead of involving the artificial intelligence-based classifiers of these common research methods. In order to prove the method proposed in this paper, the sounds of Niagara Falls, which is clearly nature sounds, recorded from near and far places are used as excitation signals to stimulate the brain. A simulation was performed to infer human emotions by analyzing and interpreting the brain waves obtained as a result of this experiment. The results of experiments and simulations performed in this paper fully demonstrate the method of extracting features from the measured EEG signals presented in this paper and inferring human emotions from the data table.

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