Robust pitch estimation is important in many areas of speech processing. In voice pathology, diverse statistics extracted form pitch estimation were commonly used to test voice quality. In this study, we compared several established pitch detection algorithms (PDAs) for verification of adequacy of the PDAs. In the database of total pathological voices of 99 and normal voices of 30, an analysis of errors related with pitch detection was evaluated between pathological and normal voices, or among the types of pathological voices. Pitch errors of all PDAs used in this study more or less showed some changes between pathological and normal voices. According to the results of pitch errors, gross pitch error showed some increases in cases of pathological voices; especially excessive increase in PDA based on nonlinear time-series. In an analysis of types of pathological voices classified by aperiodicity and the degree of chaos, the more voice has aperiodic and chaotic, the more growth of pitch errors increased. Consequently, it is required to survey the severity of tested voice in order to obtain accurate pitch estimates.

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This study is based on previous information regarding asymmetric activation in the prefrontal cortex by film-induced affects, as well as the inverse proportionality of prefrontal cortex activity to power in the alpha band of EEG. To search for a specific EEG band where the asymmetric activation in the prefrontal cortex by sound-induced affects is mainly reflected, we measured 32 college students' EEGs; 11 bands ranged from 6.5 to 35.0 Hz, at Fp1 and Fp2 sites. The power in the alpha band (8.0 to 13.0 Hz) at Fp2, especially in the alpha-2 band (9.0 to 11.0 Hz) increased while the students listened to music, during which participants reported positive affect. In contrast, the power at Fp1 increased while the students listened to noise, during which participants reported negative affect. These results imply that sound-induced positive affect increases relative left-sided activation in the prefrontal cortex, whereas induced negative affect elicits the opposite pattern of asymmetric activation.

Automatic detection of suspicious pain regions is very useful in the medical digital infrared thermal imaging research area. To detect those regions, we use the SOFES (Survival Of the Fitness kind of the Evolution Strategy) algorithm which is one of the multimodal function optimization methods. We apply this algorithm to famous diseases, such as a foot of the glycosuria, the degenerative arthritis and the varicose vein. The SOFES algorithm is available to detect some hot spots or warm lines as veins. And according to a hundred of trials, the algorithm is very fast to converge.