• Journal of the Korea Society of Computer and Information
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• v.9 no.1
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• pp.25-30
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• 2004

In this Paper, a beat recognition method from a sequence of images of conducting person was proposed. Hand position was detected using color discrimination, and symbolized by quantization. Then a motion of the conductor was represented as a sequence of symbols. HMM (Hidden Markov Model), which is excellent for recognition of sequence pattern with some level of variation, was used to recognize the sequence of symbols to be a motion for a beat.

• Proceedings of the KIEE Conference
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• 2007.04a
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• pp.334-336
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• 2007

This paper presents a beat gesture recognition method to synchronize the tempo of a robot playing a piano with the desired tempo of the user. To detect an unstructured beat gesture expressed by any part of a body, we apply an optical flow method, and obtain the trajectories of the center of gravity and normalized central moments of moving objects in images. The period of a beat gesture is estimated from the results of the fast Fourier transform. In addition, we also apply a motion control method by which robotic fingers are trained to follow a set of trajectories, Since the ability to track the trajectories influences the sound a piano generates, we adopt an iterative learning control method to reduce the tracking error.

• Proceedings of the Korean Information Science Society Conference
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• 2007.10c
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• pp.263-266
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• 2007

We introduce a vision-based hand gesture recognition fer understanding musical time and patterns without extra special devices. We suggest a simple and reliable vision-based hand gesture recognition having two features First, the motion-direction code is proposed, which is a quantized code for motion directions. Second, the conducting feature point (CFP) where the point of sudden motion changes is also proposed. The proposed hand gesture recognition system extracts the human hand region by segmenting the depth information generated by stereo matching of image sequences. And then, it follows the motion of the center of the gravity(COG) of the extracted hand region and generates the gesture features such as CFP and the direction-code finally, we obtain the current timing pattern of beat and tempo of the playing music. The experimental results on the test data set show that the musical time pattern and tempo recognition rate is over 86.42% for the motion histogram matching, and 79.75% fer the CFP tracking only.

• The Journal of the Acoustical Society of Korea
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• v.24 no.2E
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• pp.67-70
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• 2005

To realize a traditional music recognition system, some characteristics pertinent to Far East Asian music should be found. Using Spectrogram, some distinct attributes of Korean traditional music are surveyed. Frequency distribution, beat cycle and frequency energy intensity within samples have distinct characteristics of their own. Experiment is done for pre-experimentation to realize Korean traditional music recognition system. Using characteristics of Korean traditional music, $94.5\%$ of classification accuracy is acquired. As Korea, Japan and China have the same musical roots, both in instruments and playing style, analyzing Korean traditional music can be helpful in the understanding of Far East Asian traditional music.

• International Journal of Computer Science & Network Security
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• v.21 no.12spc
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• pp.415-426
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• 2021

The implementation of iris biometrics on smartphone devices has recently become an emerging research topic. As the use of iris biometrics on smartphone devices becomes more widely adopted, it is to be expected that there will be similar efforts in the research community to beat the biometric by exploring new spoofing methods and this will drive a corresponding requirement for new liveness detection methods. In this paper we addresses the problem of presentation attacks (Spoofing) against the Iris Recognition System on mobile devices and propose novel Presentation Attack Detection (PAD) method which suitable for mobile environment.

• Journal of the Korea Society of Computer and Information
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• v.15 no.2
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• pp.65-74
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• 2010

We propose a system that automatically recognizes the tense or relaxed condition of scrolling-shooting game subject that plays. Existing study compares the changed values of source of stimulation to the player by suggesting the source, and thus involves limitation in automatic classification. This study applies SOM of unsupervised learning for automatic classification and recognition of player's condition change. Application of SOM for automatic recognition of tense and relaxed condition is composed of two steps. First, ECG measurement and analysis, is to extract characteristic vector through HRV analysis by measuring ECG after having the player play the game. Secondly, SOM learning and recognition, is to classify and recognize the tense and relaxed conditions of player through SOM learning of the input vectors of heart beat signals that the characteristic extracted. Experiment results are divided into three groups. The first is HRV frequency change and the second the SOM learning results of heart beat signal. The third is the analysis of match rate to identify SOM learning performance. As a result of matching the LF/HF ratio of HRV frequency analysis to the distance of winner neuron of SOM based on 1.5, a match rate of 72% performance in average was shown.

• Smart Media Journal
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• v.8 no.3
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• pp.62-69
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• 2019

Electrocardiogram (ECG) signals cannot be counterfeited and can easily acquire signals from both wrists. In this paper, we propose a method of generating a coupling image using direction information of ECG signals as well as its usage in a personal recognition method. The proposed coupling image is generated by using forward ECG signal and rotated inverse ECG signal based on R-peak, and the generated coupling image shows a unique pattern and brightness. In addition, R-peak data is increased through the ECG signal calculation of the same beat, and it is thus possible to improve the recognition performance of the individual. The generated coupling image extracts characteristics of pattern and brightness by using the proposed convolutional neural network and reduces data size by using multiple pooling layers to improve network speed. The experiment uses public ECG data of 47 people and conducts comparative experiments using five networks with top 5 performance data among the public and the proposed networks. Experimental results show that the recognition performance of the proposed network is the highest with 99.28%, confirming potential of the personal recognition.

• The Journal of the Acoustical Society of Korea
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• v.24 no.8
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• pp.498-504
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• 2005

We introduce a Korean speech recognition platform (ECHOS) developed for education and research Purposes. ECHOS lowers the entry barrier to speech recognition research and can be used as a reference engine by providing elementary speech recognition modules. It has an easy simple object-oriented architecture, implemented in the C++ language with the standard template library. The input of the ECHOS is digital speech data sampled at 8 or 16 kHz. Its output is the 1-best recognition result. N-best recognition results, and a word graph. The recognition engine is composed of MFCC/PLP feature extraction, HMM-based acoustic modeling, n-gram language modeling, finite state network (FSN)- and lexical tree-based search algorithms. It can handle various tasks from isolated word recognition to large vocabulary continuous speech recognition. We compare the performance of ECHOS and hidden Markov model toolkit (HTK) for validation. In an FSN-based task. ECHOS shows similar word accuracy while the recognition time is doubled because of object-oriented implementation. For a 8000-word continuous speech recognition task, using the lexical tree search algorithm different from the algorithm used in HTK, it increases the word error rate by $40\%$ relatively but reduces the recognition time to half.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2007.11a
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• pp.25-28
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• 2007

본 논문은 FM 라디오 방송의 오디오 신호를 블록 단위로 음성 음악을 판별하는 시스템을 제안하는 논문이다. 본 논문에서는 음성 음악 판별 시스템을 구축하기 위해 다양한 특정 파라미터와 분류 알고리즘을 제안 한다. 특정 파라미터는 신호처리 분야(Centroid, Rolloff, Flux, ZCR, Low Energy), 음성 인식 분야(LPC, MFCC), 음악 분석 분야(MPitch, Beat)에서 각각 사용되는 파라미터를 사용하였으며 분류 알고리즘으로는 패턴인식 분야(GMM, KNN, BP)와 퍼지 신경망(ANFIS)을 사용하였고, 거리 구현은 Mahalanobis 거리를 사용하였다.

• Journal of Korea Multimedia Society
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• v.20 no.8
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• pp.1248-1257
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• 2017

We propose a frequency estimation algorithm of human movements using Kinect. We collect the 3D coordinates of the joints of a human body and then obtain the frequency-domain description of the movements using the discrete Fourier transform (DFT). By choosing the frequency with the biggest magnitude in the selected frequencies of each of human's joint, we obtain the major beat of the human movements. Experimental results show that the proposed algorithm accurately estimates the frequency of human movements. We expect that the proposed algorithm would be applied to many AR and VR applications as a preprocessing.