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In previous work, we have proposed the automatic sensitive word score detection system for a user dependent music retrieval system. However, the user dependent method causes a lot of burdens to the user because the system requires a lot of data for adapting it to each user. Hence, in this paper, we propose an automatic sensitive word score detection method for a user independent music retrieval system and evaluate the proposed method using 225 music data. Experimental results show that 87.5% of music patterns succeeded in detection of sensitive word score in the case that the difference between estimated and evaluated score is 1 (Error 1 rate). Moreover, we conduct subjective evaluation experiments to evaluate the proposed method as a utility method. From this experiment, it is observed that the user satisfaction level of the proposed method is higher than random selection impression detection.

In this paper we present a method which extracts features from palmprint images by applying the Discrete Cosine Transform (DCT) on small blocks of the segmented region of interest consisting of the middle palm area. The region is extracted after careful preprocessing to normalize for position and illumination. This method takes advantage of the well known capability of the DCT to represent natural images using only a few coefficients by performing the DCT on each block. After ranking the coefficients by magnitude and selecting only the most prominent, these are then concatenated into a compact feature vector that represents each palmprint. Recognition and verification experiments using the PolyU Palmprint Database show that this is an effective and efficient approach, with a recognition rate above 99 % and Equal Error Rate (EER) of less than 3 %.

This paper presents an improved algorithm of Incremental Simple-PCA. The Incremental Simple-PCA is a fast incremental learning algorithm based on Simple-PCA. This algorithm need not hold all training samples because it enables update of an eigenvector according to incremental samples. Moreover, this algorithm has an advantage that it can calculate the eigenvector at high-speed because matrix calculation is not needed. However, it had a problem in convergence performance of the eigenvector. Thus, in this paper, we try the improvement of this algorithm from the aspect of convergence performance. We performed computer simulations using UCI datasets to verify the effectiveness of the proposed algorithm. As a result, its availability was confirmed from the standpoint of recognition accuracy and convergence performance of the eigenvector compared with the Incremental Simple-PCA.

There is electromyogram(EMG) as one of biological signals generated along with motions of the human body. This EMG has information corresponding to condition of power, tenderness of motions and motion level. Therefore, it is thought that it is useful biological information for analyzing person’s motions. Recently, researches on this EMG have been actively done. For instance, it is used as a control signal of electrical arms, because EMG can be gathered from the remaining muscle about upper-extremity amputee. In addition, the pointing device that uses EMG has been developed. In general, EMG is measured from a part with comparatively big muscular fibers such as arms and shoulders. There is a problem that placing and removing the electrode is inconvenient when EMG is measured by placing the electrode in the arm and the shoulder. Therefore, if we can recognize wrist motions using EMG which is measured from the wrist, the range of application will extend furthermore. Currently, we have constructed a wrist motion recognition system which recognizes the wrist motion of 7 types as an object by using the technique named Simple-FLDA as a feature extraction technique. However, motions with low recognition accuracy were observed, and it was found that the difference of the recognition accuracy is significant at each motion and subject. Because EMG is highly individual and its repeatability is low. Therefore, it is necessary to deal with these problems. In this paper, we try the construction of a system that can learn by giving incremental data to achieve an online tuning. The improvement of the algorithm of Simple-FLDA that incremental learning becomes possible was tried as a technique for the construction of online tuning system. As a recognition experimental result, we can confirm the rising of the recognition accuracy by incremental learning.