• Journal of the Korea Academia-Industrial cooperation Society
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• v.14 no.7
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• pp.3218-3222
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• 2013

Pansori is our traditional vocal sound, originality and excellence in the art of conversation, gesture general became a globally recognized world intangible heritage. Especially, Pansori as shrews and humorous representation of audience participation with a high degree of artistic value and enjoy the arts throughout all layers to be responsible for the social integration of functions is evaluated. Therefore, in this paper, Pansori five yard target speech signal analysis techniques applied to analyze the Pansori acoustic features of a representation of a society and era correlation extraction studies were performed. Pansori on the five yard spectrogram, pitch, stability and strength analysis for this experiment. Pansori through experimental results Comical story while keeping the audience focused and interested to better reflect the characteristics of energy for the wave of voice and vocal cord tremor change the width of a large, stable and voice with a loud voice, that expresses were analyzed.

• Journal of Convergence Society for SMB
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• v.6 no.4
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• pp.51-57
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• 2016

This study aims at investigating and comparing the vowel lengths of the eight stressed syllable vowels among the Korean college students with the English native speakers. To do this English sentences were uttered and recorded by twenty Korean subjects. Acoustic features were measured from a sound spectrogram with the help of the Praat software program and analyzed through statistical analysis. From the results of the experiment, I was able to find out that the differences of the lengths of the first syllable stressed vowels were significant. Especially in the pronunciation of the English front low vowel /${\ae}$/, native subjects pronounced significantly longer than Korean subjects, and this result could be used as a teaching material in pronunciation class.

• Journal of Convergence for Information Technology
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• v.8 no.1
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• pp.37-42
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• 2018

This study focuses on investigating and comparing the lengths of the five words, vowels, and the ratio of the length of vowels to that of words among the Korean college students with the English native speaker. English sentences were read and recorded by Korean subjects to do this experiment. The vowel lengths were measured from a sound spectrogram, the Praat software program, and these data were analyzed through statistical analysis. I could easily tell that there were differences between the groups and they were significant. In the English front low vowel /${\ae}$/, I was able to find out that native subjects pronounced differently from Korean subjects, and the differences were significant. However, the pronunciation of the English diphthong /ai/, native subjects pronounced significantly shorter than Korean subjects.

• IEIE Transactions on Smart Processing and Computing
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• v.6 no.1
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• pp.10-17
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• 2017

We propose classification algorithms for human and dog movement. The proposed algorithms use micro-Doppler signals obtained from humans and dogs moving in four different directions. A two-stage classifier based on a support vector machine (SVM) is proposed, which uses a radial-based function (RBF) kernel and $16^{th}$-order linear predictive code (LPC) coefficients as feature vectors. With the proposed algorithms, we obtain the best classification results when a first-level SVM classifies the type of movement, and then, a second-level SVM classifies the moving object. We obtain the correct classification probability 95.54% of the time, on average. Next, to deal with the difficult classification problem of human and dog running, we propose a two-layer convolutional neural network (CNN). The proposed CNN is composed of six ($6{\times}6$) convolution filters at the first and second layers, with ($5{\times}5$) max pooling for the first layer and ($2{\times}2$) max pooling for the second layer. The proposed CNN-based classifier adopts an auto regressive spectrogram as the feature image obtained from the $16^{th}$-order LPC vectors for a specific time duration. The proposed CNN exhibits 100% classification accuracy and outperforms the SVM-based classifier. These results show that the proposed classifiers can be used for human and dog classification systems and also for classification problems using data obtained from an ultra-wideband (UWB) sensor.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.6
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• pp.244-251
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• 2014

In this study, we give a detailed account of normal and abnormal state of GIS(Gas Insulated Switch-gear) using the preventive diagnostic technology. And it is based on the analysis and diagnosis for storing data of GIS by intelligent GIS monitoring system. The wave shape of GIS sound is similar to noise and is systematically generated by discharge and its corona sound. Therefore, in this paper, to classify normal and abnormal GIS sound. We could discriminate between normal and abnormal case using level crossing rate(LCR) and spectrogram energy rate.

• The Journal of the Acoustical Society of Korea
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• v.20 no.8
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• pp.24-30
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• 2001

This article is concerned with automatic segmentation for Korean speech signals. All kinds of transition cases of phonetic units are classified into 3 types and different strategies for each type are applied. The type 1 is the discrimination of silence, voiced-speech and unvoiced-speech. The histogram analysis of each indicators which consists of wavelet coefficients and SVF (Spectral Variation Function) in wavelet coefficients are used for type 1 segmentation. The type 2 is the discrimination of adjacent vowels. The vowel transition cases can be characterized by spectrogram. Given phonetic transcription and transition pattern spectrogram, the speech signal, having consecutive vowels, are automatically segmented by the template matching. The type 3 is the discrimination of vowel and voiced-consonants. The smoothed short-time RMS energy of Wavelet low pass component and SVF in cepstral coefficients are adopted for type 3 segmentation. The experiment is performed for 342 words utterance set. The speech data are gathered from 6 speakers. The result shows the validity of the method.

• Journal of IKEEE
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• v.24 no.3
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• pp.783-790
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• 2020

In this paper, the human detecting radar system for indoor security applications is proposed, and its FPGA-based implementation results are presented. In order to minimize the complexity and memory requirements of the computation, the top half of the spectrogram was used to extract features, excluding the feature extraction techniques that require complex computation, feature extraction techniques were proposed considering classification performance and complexity. In addition, memory requirements were minimized by designing a pipeline structure without storing the entire spectrogram. Experiments on human, dog and robot cleaners were conducted for classification, and 96.2% accuracy performance was confirmed. The proposed system was implemented using Verilog-HDL, and we confirmed that a low-area design using 1140 logics and 6.5 Kb of memory was possible.

• The Journal of the Acoustical Society of Korea
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• v.39 no.5
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• pp.406-413
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• 2020

In this paper, to improve the classification accuracy of bird and amphibian acoustic sound, we utilize GLU (Gated Linear Unit) and Self-attention that encourages the network to extract important features from data and discriminate relevant important frames from all the input sequences for further performance improvement. To utilize acoustic data, we convert 1-D acoustic data to a log-Mel spectrogram. Subsequently, undesirable component such as background noise in the log-Mel spectrogram is reduced by GLU. Then, we employ the proposed temporal self-attention to improve classification accuracy. The data consist of 6-species of birds, 8-species of amphibians including endangered species in the natural environment. As a result, our proposed method is shown to achieve an accuracy of 91 % with bird data and 93 % with amphibian data. Overall, an improvement of about 6 % ~ 7 % accuracy in performance is achieved compared to the existing algorithms.

• Speech Sciences
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• v.5 no.1
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• pp.49-62
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• 1999

This study is concerned with the educational program Visual Analysis Tool (VAT) for sound development for foreign intonation using personal computer. The VAT can run on IBM-PC 386 compatible or higher. It shows the spectrogram, waveform, intensity and the pitch contour. The system can work freely on either waveform zoom in-out or the documentation of measured value. In this paper, intensity and pitch contour information were used. Twelve French sentences were recorded from a French conversational tape. And three Korean participated in this study. They spoke out twelve sentences repeatly and trid to make the same pitch contour - by visually matching their pitcgh contour to the native speaker's. A sentences were recorded again when the participants themselves became familiar with intonation, intensity and pauses. The difference of pitch contour(rising or falling), pitch value, energy, total duration of sentences and the boundary of rhythmic group between native speaker's and theirs before and after training were compared. The results were as following: 1) In a declarative sentence: a native speaker's general pitch contour falls at the end of sentences. But the participant's pitch contours were flat before training. 2) In an interrogative: the native speaker made his pitch contours it rise at the end of sentences with the exception of wh-questions (qu'est-ce que) and a pitch value varied a greath. In the interrogative 'S + V' form sentences, we found the pitch contour rose higher in comparison to other sentences and it varied a great deal. 3) In an exclamatory sentence: the pitch contour looked like a shape of a mountain. But the participants could not make it fall before or after training.

• Journal of IKEEE
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• v.24 no.1
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• pp.200-207
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• 2020

In this paper, an area-efficient feature extractor was proposed for security surveillance radar systems and FPGA-based implementation results were presented. In order to reduce the memory requirements, features extracted from Doppler profile for FFT window-size are used, while those extracted from total spectrogram for frame-size are excluded. The proposed feature extractor was design using Verilog-HDL and implemented with Xilinx Zynq-7000 FPGA device. Implementation results show that the proposed design can reduce the logic slice and memory requirements by 58.3% and 98.3%, respectively, compared with the existing research. In addition, security surveillance radar system with the proposed feature extractor was implemented and experiments to classify car, bicycle, human and kickboard were performed. It is confirmed from these experiments that the accuracy of classification is 93.4%.