The Journal of the Acoustical Society of Korea (한국음향학회지)

The Acoustical Society of Korea (한국음향학회)
권호 표지
DOI QR코드

DOI QR Code

Snoring sound detection method using attention-based convolutional bidirectional gated recurrent unit

주의집중 기반의 합성곱 양방향 게이트 순환 유닛을 이용한 코골이 소리 검출 방식

  • 김민수 (광운대학교 전자융합공학과) ;
  • 이기용 (광운대학교 전자융합공학과) ;
  • 김형국 (광운대학교 전자융합공학과)
  • Received : 2021.01.05
  • Accepted : 2021.02.24
  • Published : 2021.03.31
Download PDF
⟨ Previous Next ⟩

Abstract

This paper proposes an automatic method for detecting snore sound, one of the important symptoms of sleep apnea patients. In the proposed method, sound signals generated during sleep are input to detect a sound generation section, and a spectrogram transformed from the detected sound section is applied to a classifier based on a Convolutional Bidirectional Gated Recurrent Unit (CBGRU) with attention mechanism. The applied attention mechanism improved the snoring sound detection performance by extending the CBGRU model to learn discriminative feature representation for the snoring detection. The experimental results show that the proposed snoring detection method improves the accuracy by approximately 3.1 % ~ 5.5 % than existing method.

본 논문은 수면 무호흡 환자의 중요한 증상 중의 하나인 코골이 사운드 자동 검출 방식을 제안한다. 제안된 방식에서는 수면 중 발생하는 소리 신호를 입력받아 소리 발생 구간을 검출하고, 검출된 소리 구간으로부터 변환된 스펙트로그램을 주의집중 기반의 합성곱 양방향 게이트 순환 유닛 기반의 분류기에 적용하였다. 적용된 주의집중 메커니즘은 합성곱 양방향 게이트 순환 유닛 모델을 확장하여 코골이 소리에 대한 차별적 특징 표현을 학습함으로써 코골이 검출 성능을 향상시켰다. 실험 결과는 제안하는 코골이 검출 방식이 기존 방식보다 약 3.1 % ~ 5.5 %의 정확도 향상을 보여준다.

Keywords

References

  1. J. A. Fiz, J. Abad, R. Jane, M. Riera, M. A. Mananas, P. Caminal, D. Rodenstein, and J. Morera, "Acoustic analysis of snoring sound in patients with simple snoring and obstructive sleep apnoea," Eur. Respir. J. 9, 2365-2370 (1996). https://doi.org/10.1183/09031936.96.09112365
  2. J. Sola-Soler, R. Jane, J. A. Fiz, and J. Morera, "Spectral envelope analysis in snoring signals from simple snorers and patients with obstructive sleep apnea," Proc. IEEE-EMBS, 3, 2527-2530 (2003).
  3. J. Kim, T. Kim, D. Lee, J. W. Kim, and K. Lee, "Exploiting temporal and nonstationary features in breathing sound analysis for multiple obstructive sleep apnea severity classification," Biomed. Eng. Online. 16, 1-18 (2017). https://doi.org/10.1186/s12938-016-0292-9
  4. B. Arsenali, J. V. Dijk, O. Ouweltjes, B. D. Brinker, D. Pervernagie, R. Krijn, M. V. Gilst, and S. Overeem, "Recurrent neural network for classification of snoring and non-snoring sound events," Proc. 40th Ann. Int. Conf. IEEE EMBC. 328-331 (2018).
  5. S. J. Lim, S. J. Jang, J. Y. Lim, and J. H. Ko, "Classification of snoring sound based on a recurrent neural network," Expert Syst. Appl. 123, 237-245 (2019). https://doi.org/10.1016/j.eswa.2019.01.020
  6. F. Vesperini, L. Romeo, E. Principi, A. Monteriu, and S. Squartini, "Convolutional recurrent neural networks and acoustic data augmentation for snore detection," Neural Approaches to Dynamics of Signal Exchanges, 151, 35-46 (2020). https://doi.org/10.1007/978-981-13-8950-4_4
  7. Z. Zhang, S. Xu, S. Zhang, T. Qiao, and S. Cao, "Attention based convolutional recurrent neural network for environmental sound classification," Proc. PRCV. 261-271 (2019).
  8. S. H. Ryu and H. G. Kim, "Audio mixer algorithm for enhancing speech quality of multi-party audio telephony" (in Korea), J. Acoust. Soc. Kr. 32, 541-547 (2013). https://doi.org/10.7776/ASK.2013.32.6.541
  9. D. P. Kingma and J. Ba. "Adam: a method for stochastic optimization," arXiv preprint arXiv: 1412.6980 (2014).