• Journal of Sasang Constitutional Medicine
• /
• v.17 no.2
• /
• pp.46-51
• /
• 2005

1. Objectives The purpose of this study is to objectify the diagnosis of Sasang Constitution. Using the methods of this study, it will improve to classificate Sasang Constitution. 2. Methods 1) Automatic feature extraction of human frontal faces for Sasang Constitution classification. 2) Color feature extraction of human frontal faces (1)Erosion filtering (skin-white, the other-black) (2) Median median 3. Results and Conclusions Observing a person's shape has been the major method for Sasang Constitution classification, which usually has been dependent upon doctor's intuition as of these days. We are developing an automatic system which provides objective basic data for Sasang Constitution classification. For this, in this paper, firstly, the signal processing techniques are applied to automatic feature extraction of human frontal faces for Sasang Constitution classification. The experiment is conducted to verify the effectiveness of the proposed system.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.22 no.6
• /
• pp.793-798
• /
• 2012

In this paper, we explored the new method for extracting feature from the electroencephalography (EEG) signal based on linear regression technique with the orthonormal polynomial bases. At first, EEG signals from electrodes around motor cortex were selected and were filtered in both spatial and temporal filter using band pass filter for alpha and beta rhymic band which considered related to the synchronization and desynchonization of firing neurons population during motor imagery task. Signal from epoch length 1s were fitted into linear regression with Legendre polynomials bases and extract the linear regression weight as final features. We compared our feature to the state of art feature, power band feature in binary classification using support vector machine (SVM) with 5-fold cross validations for comparing the classification accuracy. The result showed that our proposed method improved the classification accuracy 5.44% in average of all subject over power band features in individual subject study and 84.5% of classification accuracy with forward feature selection improvement.

• The Journal of the Acoustical Society of Korea
• /
• v.22 no.4E
• /
• pp.141-149
• /
• 2003

As music of digital form has been widely used, many people have been interested in the automatic extraction of natural information of music itself, such as key of a music, chord progression, melody progression, tempo, etc. Although some studies have been tried, consistent and reliable results of musical information extraction had not been achieved. In this paper, we propose a method to extract chord and tempo information from general polyphonic music signals. Chord can be expressed by combination of some musical notes and those notes also consist of some frequency components individually. Thus, it is necessary to analyze the frequency components included in musical signal for the extraction of chord information. In this study, we utilize a sinusoidal modeling, which uses sinusoids corresponding to frequencies of musical tones, and show reliable chord extraction results of sinusoidal modeling. We could also find that the tempo of music, which is the one of remarkable feature of music signal, interactively supports the chord extraction idea, if used together. The proposed scheme of musical feature extraction is able to be used in many application fields, such as digital music services using queries of musical features, the operation of music database, and music players mounting chord displaying function, etc.

• Proceedings of the Acoustical Society of Korea Conference
• /
• 1993.06a
• /
• pp.169-173
• /
• 1993

In this paper, the features extraction of reflected signals from various type of defects existing in the solid has been studied by Wiener filtering technique. In this experiment, three types of the defect have been considered; a flat cut, a angular cut and a circular hole. All of the defects have the same size, 20mm in diameter and have been located at 45mm in depth from the aluminum surface. In the result of the experiment, it has been found that the wiener filtering technique used for features extraction from the reflected signal corresponding to each defect have been very effective for defect classification.

• Journal of Ocean Engineering and Technology
• /
• v.28 no.6
• /
• pp.552-559
• /
• 2014

In general, the number of underwater transient signals is very limited for research on automatic recognition. Data-dependent feature extraction is one of the most effective methods in this case. Therefore, we suggest WPCC (Wavelet packet ceptsral coefficient) as a feature extraction method. A wavelet packet best tree for each data set is formed using an entropy-based cost function. Then, every terminal node of the best trees is counted to build a common wavelet best tree. It corresponds to flexible and non-uniform filter bank reflecting characteristics for the data set. A GMM (Gaussian mixture model) is used to classify five classes of underwater transient data sets. The error rate of the WPCC is compared using MFCC (Mel-frequency ceptsral coefficients). The error rates of WPCC-db20, db40, and MFCC are 0.4%, 0%, and 0.4%, respectively, when the training data consist of six out of the nine pieces of data in each class. However, WPCC-db20 and db40 show rates of 2.98% and 1.20%, respectively, while MFCC shows a rate of 7.14% when the training data consists of only three pieces. This shows that WPCC is less sensitive to the number of training data pieces than MFCC. Thus, it could be a more appropriate method for underwater transient recognition. These results may be helpful to develop an automatic recognition system for an underwater transient signal.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.11 no.11
• /
• pp.4473-4482
• /
• 2010

The examination of pulse, which is a typical palpation technique in the oriental medicine, has been used conventional analog system for discrimination of 28 pulses. However, the clipping phenomenon in the pulses, which used same feature extraction technique with ECG signals, has been occurred in analog system due to feature extraction method and over amplification from the input signals. It caused inaccurate to analyze the pulse signals. In this paper, we propose a digital filter design technique based on Prony's method for signal modeling and C-spline interpolation for feature extraction from pulse signal to compensate analog pulse detection system. In addition, we suggest a compensated electronic pulse detection system comprising new pulse analyzing algorithm and shape analysis technique for pulses, which were difficult to use in analog system. The feasibility for new proposed system has been confirmed comparing output signals between electronic pulse detection system having proposed filter design techniques with pulse analyzing algorithm and conventional analog system.

• ETRI Journal
• /
• v.29 no.3
• /
• pp.292-299
• /
• 2007

In this paper, we present a speech recognition system using a throat microphone. The use of this kind of microphone minimizes the impact of environmental noise. Due to the absence of high frequencies and the partial loss of formant frequencies, previous systems using throat microphones have shown a lower recognition rate than systems which use standard microphones. To develop a high performance automatic speech recognition (ASR) system using only a throat microphone, we propose two methods. First, based on Korean phonological feature theory and a detailed throat signal analysis, we show that it is possible to develop an ASR system using only a throat microphone, and propose conditions of the feature extraction algorithm. Second, we optimize the zero-crossing with peak amplitude (ZCPA) algorithm to guarantee the high performance of the ASR system using only a throat microphone. For ZCPA optimization, we propose an intensification of the formant frequencies and a selection of cochlear filters. Experimental results show that this system yields a performance improvement of about 4% and a reduction in time complexity of 25% when compared to the performance of a standard ZCPA algorithm on throat microphone signals.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.11 no.12
• /
• pp.6038-6053
• /
• 2017

In this study, we propose an automatic melody extraction algorithm using deep learning. In this algorithm, feature images, generated using the energy of frequency band, are extracted from polyphonic audio files and a deep learning technique, a convolutional neural network (CNN), is applied on the feature images. In the training data, a short frame of polyphonic music is labeled as a musical note and a classifier based on CNN is learned in order to determine a pitch value of a short frame of audio signal. We want to build a novel structure of melody extraction, thus the proposed algorithm has a simple structure and instead of using various signal processing techniques for melody extraction, we use only a CNN to find a melody from a polyphonic audio. Despite of simple structure, the promising results are obtained in the experiments. Compared with state-of-the-art algorithms, the proposed algorithm did not give the best result, but comparable results were obtained and we believe they could be improved with the appropriate training data. In this paper, melody extraction and the proposed algorithm are introduced first, and the proposed algorithm is then further explained in detail. Finally, we present our experiment and the comparison of results follows.

• ETRI Journal
• /
• v.35 no.1
• /
• pp.100-108
• /
• 2013

In this paper, a feature extraction (FE) method is proposed that is comparable to the traditional FE methods used in automatic speech recognition systems. Unlike the conventional spectral-based FE methods, the proposed method evaluates the similarities between an embedded speech signal and a set of predefined speech attractor models in the reconstructed phase space (RPS) domain. In the first step, a set of Gaussian mixture models is trained to represent the speech attractors in the RPS. Next, for a new input speech frame, a posterior-probability-based feature vector is evaluated, which represents the similarity between the embedded frame and the learned speech attractors. We conduct experiments for a speech recognition task utilizing a toolkit based on hidden Markov models, over FARSDAT, a well-known Persian speech corpus. Through the proposed FE method, we gain 3.11% absolute phoneme error rate improvement in comparison to the baseline system, which exploits the mel-frequency cepstral coefficient FE method.

• Proceedings of the KSME Conference
• /
• 2001.06a
• /
• pp.609-615
• /
• 2001

This study proposes analysis and evaluation method of time series ultrasonic signal using attractor analysis for fusion joint part of polyethylene piping. Quantitatively characteristics of fusion joint part is analysed features extracted from time series. Trajectory changes in the attractor indicated a substantial difference in fractal characteristics. These differences in characteristics of fusion joint part enables the evaluation of unique characteristics of fusion joint part. In quantitative fractal feature extraction, feature values of 4.291 in the case of debonding and 3.694 in the case of bonding were proposed on the basis of fractal dimensions. In quantitative quadrant feature extraction, 1,306 point in the case of bonding(one quadrant) and 1,209 point(one quadrant) in the case of debonding were proposed on the basis of fractal dimensions. Proposed attractor feature extraction can be used for integrity evaluation of polyethylene piping material which is in case of bonding or debonding.