Journal of information and communication convergence engineering

  • 제22권2호
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  • Pages.145-152
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  • 2024
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  • 2234-8255(pISSN)
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  • 2234-8883(eISSN)
한국정보통신학회 (The Korea Institute of Information and Commucation Engineering)
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Prediction of Closed Quotient During Vocal Phonation using GRU-type Neural Network with Audio Signals

  • Hyeonbin Han (Department of Mathematical Data Science, Hanyang University ERICA) ;
  • Keun Young Lee (Independent scholar) ;
  • Seong-Yoon Shin (School of Computer Science and Engineering, Kunsan National University) ;
  • Yoseup Kim (Digital Healthcare Research Center, Deltoid Inc.) ;
  • Gwanghyun Jo (Department of Mathematical Data Science, Hanyang University ERICA) ;
  • Jihoon Park (Division of Vocal Music, Nicedream Music Academy) ;
  • Young-Min Kim (Digital Health Research Divisions, Korea Institute of Oriental Medicine)
  • 투고 : 2024.03.30
  • 심사 : 2024.06.07
  • 발행 : 2024.06.30
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초록

Closed quotient (CQ) represents the time ratio for which the vocal folds remain in contact during voice production. Because analyzing CQ values serves as an important reference point in vocal training for professional singers, these values have been measured mechanically or electrically by either inverse filtering of airflows captured by a circumferentially vented mask or post-processing of electroglottography waveforms. In this study, we introduced a novel algorithm to predict the CQ values only from audio signals. This has eliminated the need for mechanical or electrical measurement techniques. Our algorithm is based on a gated recurrent unit (GRU)-type neural network. To enhance the efficiency, we pre-processed an audio signal using the pitch feature extraction algorithm. Then, GRU-type neural networks were employed to extract the features. This was followed by a dense layer for the final prediction. The Results section reports the mean square error between the predicted and real CQ. It shows the capability of the proposed algorithm to predict CQ values.

키워드

과제정보

This study was supported by a grant (NRF KSN1824130) from the Korea Institute of Oriental Medicine.

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