Dergiler / International Journal of Intelligent Systems and Applications in Engineering / 2019 / Cilt: 7 - Sayı: 4

Detecting of Warning Sounds in the Traffic using Linear Predictive Coding

Sayfa
195–200
DOI
—
🏛️Bu makalenin bibliyografik verileri ve tam metin bağlantısı DergiPark üzerinden sağlanmıştır.
DergiPark Kaydı ↗
✨

Yapay Zeka Hızlı Bakış & Çıkarımlar

AI Insight
⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

For the sound which has been detected to be warning sound, information about the sound direction has been given to user.

📌 3 Maddede Bu Makale

🎯
Amaç & Hipotez:Providing attendance of disabled persons into life and increasing it is an important social issue.
📊
Temel Bulgular & Veri:In this context detecting sirens and sounds of those vehicles which have priority of way in traffic such as ambulance, fire-fighting vehicle and police car will enable to hearing disabled people to drive more comfortably.
💡
Sonuç & Katkı:For the sound which has been detected to be warning sound, information about the sound direction has been given to user.
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Alan Parametreleri

Özet

Providing attendance of disabled persons into life and increasing it is an important social issue. In this context detecting sirens and sounds of those vehicles which have priority of way in traffic such as ambulance, fire-fighting vehicle and police car will enable to hearing disabled people to drive more comfortably. Recognising such warning sounds and detecting their direction have been aimed in this study. Sirens of ambulance, police car and fire-fighting vehicle in traffic have been classified as positive samples for application. Noises such as music and traffic noises have been classified as negative. Linear Estimator Coding has been made up by converting sound signals into digital data and qualities that reflect the sound in the best way has been determined by removing the qualities that do not reflect the sound in the data. By using principal component analysis method, meaningful and those qualities that represent the data in the best way have been classified by K Nearest Neighbor and Support Vector Machine Algorithm. After creating model about sound detection, model has been tested by performing new sound records. For the sound which has been detected to be warning sound, information about the sound direction has been given to user.

🔖

Bu Makaleye Atıf Yap

AKYÜREK, C., SARAÇOĞLU, R. (2019). Detecting of Warning Sounds in the Traffic using Linear Predictive Coding. International Journal of Intelligent Systems and Applications in Engineering, Cilt: 7 - Sayı: 4, 195–200. https://ajindex.com/articles/161619

📄 Tam Metin PDF Okuyucu

📄

PDF görüntüleyici hazırlanıyor...

Kaynakça (16)

  1. Türkiye Sağlık Araştırması. Türkiye İstatistik Kurumu, Ankara [Online]. Available:http://www.tuik.gov.tr/PreTablo.do?alt_id=1017, Accessed on: Apr. 30, 2019
  2. Harmancı, S., DHA, Amasya, Türkiye [Online]. Available: http://www.hurriyet.com.tr/isitme-engelli-ogrencilere-isaret-diliyletrafi- 40274838. Accessed on: Apr. 30, 2019
  3. Ö. Aydın, “Yapay sinir ağlarını kullanarak bir ses tanıma sistemi geliştirilmesi” M.S. thesis, Computer Engineering, Trakya University, Edirne, Turkey, 2005
  4. M. İnal, “Yapay Sinir Ağları Tabanlı Konuşmacı Tanıma,” Ph.D. dissertation, Electric and Computer Education, Kocaeli University, Kocaeli, Turkey, 2001
  5. W. M. Campbell, K. T. Assaleh, C. C. Broun, “Speaker Recognition with Polynomial Classifiers,” 2002
  6. F. Ertaş, C. Hanilçi, “A New Classifier For Speaker Identification System,” TMMOB Elektrik Mühendisleri Odası, Ankara, Turkey, 2007.[Online].Available:http://www.emo.org.tr/ekler/1c207d3fccca3 a1_ek.pdf. Accessed on: May 04, 2019
  7. C. Hanilçi, “Konuşmacı Tanıma Yöntemlerinin Karşılaştırmalı Analizi,” M.S. thesis, Electronic Engineering, Uludağ University, Bursa, Turkey, 2007
  8. S. Başaran, “Yapay Sinir Ağları Kullanarak Konuşmacı Tanıma,” M.S. thesis, Electronic Engineering, Uludağ University, Bursa, Turkey, 2007
  9. W. M. Campbell., J. P. Campbell, D. A. Reynolds, E. Singer, Torres- P. A. Carrasquillo, “Support vector machines for speaker and language recognition,” Computer Speech & Language, 20(2-3), 210- 229, 2006
  10. G. Dede and M. H. Sazlı, “Biyometrik Sistemlerin Örüntü Tanıma Perspektifinden İncelenmesi Ve Ses Tanıma Modülü Simülasyonu,” EEBM Ulusal Kongresi, 2010
  11. C. Hanilçi, “Konuşmacı Tanımada Map Uyarlamalı Sınıflandırıcılar,” Ph.D. dissertation, Electronic Engineering, Uludağ University, Bursa, Turkey, 2013
  12. H. Uğuz, A. Arslan, “A new approach based on discrete hidden Markov model using Rocchio algorithm for the diagnosis of the brain diseases,” Digital Signal Processing, 3(20), 923-934, 2010
  13. C. Ünsalan and A. Erçil, “Comparison of Feature Selection Algorithms A New Performance Criteria for Feature Selection,” Proceedings of IEEE SIU'98, pp. 60-65, 1998
  14. C. Cortes and V. Vapnik, “Support-vector networks,” Machine learning, 20(3), 273-297, 1995
  15. O. Kaynar, H. Arslan, Y. Görmez, Y. E. Işık, “Makine Öğrenmesi ve Öznitelik Seçim Yöntemleriyle Saldırı Tespiti,” Bilişim Teknolojileri Dergisi, 11(2), 175-185, 2018
  16. Ö. Eskidere, “A Comparison of Feature Sele