• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2012년도 춘계학술대회 논문집
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• pp.881-882
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• 2012

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권2호
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• pp.713-725
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• 2022

Mostly, artificial intelligence does not show any definite change in emotions. For this reason, it is hard to demonstrate empathy in communication with humans. If frequency modification is applied to neutral emotions, or if a different emotional frequency is added to them, it is possible to develop artificial intelligence with emotions. This study proposes the emotion conversion using the Generative Adversarial Network (GAN) based voice frequency synthesis. The proposed method extracts a frequency from speech data of twenty-four actors and actresses. In other words, it extracts voice features of their different emotions, preserves linguistic features, and converts emotions only. After that, it generates a frequency in variational auto-encoding Wasserstein generative adversarial network (VAW-GAN) in order to make prosody and preserve linguistic information. That makes it possible to learn speech features in parallel. Finally, it corrects a frequency by employing Amplitude Scaling. With the use of the spectral conversion of logarithmic scale, it is converted into a frequency in consideration of human hearing features. Accordingly, the proposed technique provides the emotion conversion of speeches in order to express emotions in line with artificially generated voices or speeches.

• Journal of information and communication convergence engineering
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• 제19권3호
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• pp.148-154
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• 2021

With the advent of context-aware computing, many attempts were made to understand emotions. Among these various attempts, Speech Emotion Recognition (SER) is a method of recognizing the speaker's emotions through speech information. The SER is successful in selecting distinctive 'features' and 'classifying' them in an appropriate way. In this paper, the performances of SER using neural network models (e.g., fully connected network (FCN), convolutional neural network (CNN)) with Mel-Frequency Cepstral Coefficients (MFCC) are examined in terms of the accuracy and distribution of emotion recognition. For Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) dataset, by tuning model parameters, a two-dimensional Convolutional Neural Network (2D-CNN) model with MFCC showed the best performance with an average accuracy of 88.54% for 5 emotions, anger, happiness, calm, fear, and sadness, of men and women. In addition, by examining the distribution of emotion recognition accuracies for neural network models, the 2D-CNN with MFCC can expect an overall accuracy of 75% or more.

• 한국감성과학회:학술대회논문집
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• 한국감성과학회 2000년도 춘계 학술대회 및 국제 감성공학 심포지움 논문집 Proceeding of the 2000 Spring Conference of KOSES and International Sensibility Ergonomics Symposium
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• pp.99-103
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• 2000

There are two approaches in the study of emotion in the physiological psychology. The first is to clarify the brain mechanism of emotion, and the second is to evaluate objectively emotions using physiological responses along with our feeling experience. The method presented here belongs to the second one. Our method is based on the "level-crossing point detection" method. which involves the analysis of frequency fluctuations of EEG and is characterized by estimation of emotionality using coefficients of slopes in the log-power spectra of frequency fluctuation in alpha waves on both the left and right frontal lobe. In this paper we introduce a new theory of estimation on an individual's emotional state by using our non-invasive and easy measurement apparatus.

• 디지털융복합연구
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• 제11권11호
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• pp.381-387
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• 2013

최근 인간의 감성에 반응하고, 감성을 유도하는 감성콘텐츠가 문화산업 분야에서 크게 주목을 받으면서 멀티미디어 콘텐츠가 유발하는 감성 추출에 초점이 모아지고 있다. 게다가 최근 멀티미디어 콘텐츠가 빠르고 방대하게 생산, 유통되는 흐름으로 볼 때 콘텐츠에서 유발하는 감성을 자동으로 추출하는 기법의 연구들이 주목받고 있다. 본 논문은 멀티미디어 콘텐츠의 소리 정보 중 특정 주파수대역의 볼륨 값을 활용하여 멀티미디어 콘텐츠 내의 감성지수를 추출하는 방법에 대해 연구하고자 한다. 이러한 연구는 동영상 콘텐츠의 감성지수를 자동으로 추출할 수 있도록 하며 추출된 정보를 활용하여 사용자의 현재 감성, 혹은 날씨 등과 같은 기타 요소에 맞추어 사용자에게 맞춤형 콘텐츠를 제공하는데 사용되어질 것이다.

• 한국정보시스템학회지:정보시스템연구
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• 제29권3호
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• pp.237-251
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• 2020

Purpose The research was studied the hierarchical Hangul emotion index by organizing all the emotions which SNS users are thinking. As a preliminary study by the researcher, the English-based Plutchick (1980)'s emotional standard was reinterpreted in Korean, and a hashtag with implicit meaning on SNS was studied. To build a multidimensional emotion dictionary and classify three-dimensional emotions, an emotion seed was selected for the composition of seven emotion sets, and an emotion word dictionary was constructed by collecting SNS hashtags derived from each emotion seed. We also want to explore the priority of each Hangul emotion index. Design/methodology/approach In the process of transforming the matrix through the vector process of words constituting the sentence, weights were extracted using TF-IDF (Term Frequency Inverse Document Frequency), and the dimension reduction technique of the matrix in the emotion set was NMF (Nonnegative Matrix Factorization) algorithm. The emotional dimension was solved by using the characteristic value of the emotional word. The cosine distance algorithm was used to measure the distance between vectors by measuring the similarity of emotion words in the emotion set. Findings Customer needs analysis is a force to read changes in emotions, and Korean emotion word research is the customer's needs. In addition, the ranking of the emotion words within the emotion set will be a special criterion for reading the depth of the emotion. The sentiment index study of this research believes that by providing companies with effective information for emotional marketing, new business opportunities will be expanded and valued. In addition, if the emotion dictionary is eventually connected to the emotional DNA of the product, it will be possible to define the "emotional DNA", which is a set of emotions that the product should have.

• 한국정밀공학회지
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• 제23권5호
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• pp.68-76
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• 2006

The ability to recognize human emotion is one of the hallmarks of human-robot interaction. Hence this paper describes the realization of emotion recognition. For emotion recognition from voice, we propose a new feature called frequency range of meaningful signal. With this feature, we reached average recognition rate of 76% in speaker-dependent. From the experimental results, we confirm the usefulness of the proposed feature. We also define the noise environment and conduct the noise-environment test. In contrast to other features, the proposed feature is robust in a noise-environment.

• 한국의류학회지
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• 제23권2호
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• pp.314-325
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• 1999

The purposes of this study were to classify the types of consumers' emotion experienced in-store by shopping motivation and to examined the effects of store environmental factors on emotion and on shopping behavior. The questionnaires were administered to 330 women shopped in department store. Data from 299 women were analyzed by using frequency t-test and regression analysis by SPSS for windows PC program The results of this study were as follows : 1. The consumers' emotion experienced in -store were composed of five factors. But it could be divided by positive negative factors. 2. There was not significant difference in positive emotion between he Product Pu-rchasing Motive Group and the Window Shopping Motive Group. But there was significant difference n negative emotion between two groups. 3. It was found that the effect of environmental factors of apparel store on emotion was significant in both groups. Especially salespeoples' pressure significantly influenced negative emotion in both groups. 4. The emotion experienced in -store significantly influenced clothing shopping behavior.

• 음성과학
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• 제12권4호
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• pp.53-70
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• 2005

A Vowel of speech signals are multicomponent signals composed of AM-FM components whose instantaneous frequency and instantaneous amplitude are time-varying. The changes of emotion states cause the variation of the instantaneous frequencies and the instantaneous amplitudes of AM-FM components. Therefore, it is important to estimate exactly the instantaneous frequencies and the instantaneous amplitudes of AM-FM components for the extraction of key information representing emotion states and changes in speech signals. In tills paper, firstly a method decomposing speech signals into AM - FM components is addressed. Secondly, the fundamental frequency of vowel sound is estimated by the simple method based on the spectrogram. The estimate of the fundamental frequency is used for decomposing speech signals into AM-FM components. Thirdly, an estimation method is suggested for separation of the instantaneous frequencies and the instantaneous amplitudes of the decomposed AM - FM components, based on Hilbert transform and the demodulation property of the extended Fourier transform. The estimates of the instantaneous frequencies and the instantaneous amplitudes can be used for modification of the spectral distribution and smooth connection of two words in the speech synthesis systems based on a corpus.

• 한국콘텐츠학회논문지
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• 제14권10호
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• pp.463-471
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• 2014

본 논문에서는 근적외선 카메라를 이용한 눈의 다양한 특징 분석을 통해 감성을 분류하는 방법에 관한 연구를 진행하였다. 제안하는 방법은 기존의 유사한 연구와 비교했을 때, 감성 분류를 위해 더 많은 눈의 특징을 사용하였고, 각 특징이 모두 유의미한 정보를 포함하고 있음을 검증하였다. 긍정-부정, 각성-이완의 상반된 감성 유발을 위해 청각 자극을 사용함으로써, 눈의 특징에 끼치는 영향을 최소화하였다. 감성 분류를 위한 특징으로써, 동공 크기, 동공 크기 변화율, 깜박임 빈도, 눈을 감은 지속시간을 사용하였으며, 이들은 근적외선 카메라 영상으로부터 자체 개발한 자동화된 처리 방법을 통해 추출된다. 분석 결과, 각성-이완 감성 유발 자극에 대해서는 동공 크기 변화율과 깜박임 빈도 특징이 유의한 차이를 보였다. 또한, 긍정-부정 감성 유발 자극에 대해에서는 눈을 감은 지속시간 특징이 유의한 차이를 보였다. 특히 동공 크기 특징은 각성-이완, 긍정-부정의 상반된 감성 자극 유발 상황에서 모두 유의한 차이가 없음을 확인할 수 있었다.