전기학회논문지 (The Transactions of The Korean Institute of Electrical Engineers)

  • 제65권7호
  • /
  • Pages.1263-1269
  • /
  • 2016
  • /
  • 1975-8359(pISSN)
  • /
  • 2287-4364(eISSN)
대한전기학회 (The Korean Institute of Electrical Engineers)
권호 표지
DOI QR코드

DOI QR Code

청각장애인을 위한 웨어러블 기기의 위험소리 검출 엔진 설계

A Design of Dangerous Sound Detection Engine of Wearable Device for Hearing Impaired Persons

  • 투고 : 2016.06.02
  • 심사 : 2016.06.27
  • 발행 : 2016.07.01
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

Hearing impaired persons are exposed to the danger since they can't be aware of many dangerous situations like fire alarms, car hones and so on. Therefore they need haptic or visual informations when they meet dangerous situations. In this paper, we design a dangerous sound detection engine for hearing impaired. We consider four dangerous indoor situations such as a boiled sound of kettle, a fire alarm, a door bell and a phone ringing. For outdoor, two dangerous situations such as a car horn and a siren of emergency vehicle are considered. For a test, 6 data sets are collected from those six situations. we extract LPC, LPCC and MFCC as feature vectors from the collected data and compare the vectors for feasibility. Finally we design a matching engine using an artificial neural network and perform classification tests. We perform classification tests for 3 times considering the use outdoors and indoors. The test result shows the feasibility for the dangerous sound detection.

키워드

참고문헌

  1. Jae-Hun Choi and Joon-Hyuk Chang "Sound Reinforcement Based on Context Awareness for Hearing Impaired" The Institute of Electronics and Information Engineers Vol.48, No.5, pp.109-114, September, 2011.
  2. Y. Toyoda, J. Huang, S. Ding and Y. Liu. "Environmental sound recognition by multilayered neural networks" in Poc. International Conference on Computer and Information Technology, pp. 123-127, September. 2004.
  3. Ji-Eun Kim and Ho-Sub Yoon. "Three sounds classification to recognize dangerous situation for the hearing-impaired" The conference on Jounal of the HCI Society of Korea, pp. 570-572, January. 2011.
  4. Oppenhiem, Schafer, "discrete-time signal processing 3 edition", Pearson, 2007.
  5. Haykin, Simon, adaptive filter theory (5th edition), Pearson, 2013.
  6. Y. Tyoda, J. Huang, S. Ding and Y. Liu, "Environmental sound recognition by multilayered neural networks", in Proc. International Conference on Computer and Information Technology, pp.123-127, 2004,
  7. Sung-Woo Byun, So-min Lee, Seok-Pil Lee, "A Selection of Optimal EEG Channel for Emotion Analysis According to Music Listening using Stochastic Variables", KIEE, Vol. 62, No. 11, pp 1598-1603, 2013.
  8. Ha-Na Choi, Sung-Woo Byun and Seok Pil Lee, "Discriminative Feature Vector Selection for Emotion Classification Based on Speech", The Transactions of The Korea Institute of Electrical Engineers, Vol.64, No.9, pp.1363-1368 2015 https://doi.org/10.5370/KIEE.2015.64.9.1363
  9. So-Min Lee, Sung-Woo Byun, Seok-Pil Lee, "Comparison of EEG Feature Vector for Emotion Classification according to Music Listening", KIEE, Vol. 63, No. 5, pp 696-702, 2014.
  10. Kazuhide Okada, Gwan Kim, Pyong Sik Pak, "Sound Information Notification System by Two-Channel Electrotactile Stimulation for Hearing Impaired Persons", 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp.3826-3829 2007.
  11. cs231n, http://cs231n.github.io/convolutional-networks/, (accessed June 20, 2016).
  12. Wikipedia, https://en.wikipedia.org/wiki/Levinson_recursion, (accessed June 20, 2016).