Detecting of Warning Sounds in the Traffic using Linear Predictive Coding

Keywords: Linear Predictive Coding, Principal Component Analysis, Support Vector Machine, Sound Classification

Abstract

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 soundin 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.

Downloads

Download data is not yet available.

References

Türkiye Sağlık Araştırması. Türkiye İstatistik Kurumu, Ankara [Online]. Available:http://www.tuik.gov.tr/PreTablo.do?alt_id=1017, Accessed on: Apr. 30, 2019.

Harmancı, S., DHA, Amasya, Türkiye [Online]. Available: http://www.hurriyet.com.tr/isitme-engelli-ogrencilere-isaret-diliyle-trafi-40274838. Accessed on: Apr. 30, 2019.

Ö. Aydın, “Yapay sinir ağlarını kullanarak bir ses tanıma sistemi geliştirilmesi” M.S. thesis, Computer Engineering, Trakya University, Edirne, Turkey, 2005.

M. İnal, “Yapay Sinir Ağları Tabanlı Konuşmacı Tanıma,” Ph.D. dissertation, Electric and Computer Education, Kocaeli University, Kocaeli, Turkey, 2001.

W. M. Campbell, K. T. Assaleh, C. C. Broun, “Speaker Recognition with Polynomial Classifiers,” 2002.

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/1c207d3fccca3a1_ek.pdf. Accessed on: May 04, 2019.

C. Hanilçi, “Konuşmacı Tanıma Yöntemlerinin Karşılaştırmalı Analizi,” M.S. thesis, Electronic Engineering, Uludağ University, Bursa, Turkey, 2007.

S. Başaran, “Yapay Sinir Ağları Kullanarak Konuşmacı Tanıma,” M.S. thesis, Electronic Engineering, Uludağ University, Bursa, Turkey, 2007.

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.

G. Dede and M. H. Sazlı, “Biyometrik Sistemlerin Örüntü Tanıma Perspektifinden İncelenmesi Ve Ses Tanıma Modülü Simülasyonu,” EEBM Ulusal Kongresi, 2010.

C. Hanilçi, “Konuşmacı Tanımada Map Uyarlamalı Sınıflandırıcılar,” Ph.D. dissertation, Electronic Engineering, Uludağ University, Bursa, Turkey, 2013.

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.

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.

C. Cortes and V. Vapnik, “Support-vector networks,” Machine learning, 20(3), 273-297, 1995.

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.

Ö. Eskidere, “A Comparison of Feature Selection Methods for Diagnosis Of Parkinson’s Disease From Vocal Measurements,” Sigma, 30, 402-414, 2012.

M. Karakoyun and M. Hacıbeyoğlu, “Biyomedikal Veri Kümeleri İle Makine Öğrenmesi Siniflandirma Algoritmalarinin İstatistiksel Olarak Karşilaştirilmasi,” Dokuz Eylül Üniversitesi Mühendislik Fakültesi Fen ve Mühendislik Dergisi, 16(48), 30-42, 2014.

İ. F. Pilavcılar, “Metin Madenciliği İle Metin Sınıflandırma,” M.S. thesis, Mathematics Engineering, Yıldız Teknik University, İstanbul, Turkey, 2007.

E. Taşcı and A. Onan, “K-en yakın komşu algoritması parametrelerinin sınıflandırma performansı üzerine etkisinin incelenmesi,” Akademik Bilişim, 2016.

M. K. Baygün and A. K. Yaldir, “Linear predictive coding ve dynamic time warping teknikleri kullanilarak ses tanima sistemi geliştirilmesi."

K. Yıldız, Y. Çamurcu and B. Doğan, “Veri madenciliğinde temel bileşenler analizi ve Negatifsiz matris çarpanlarına ayırma tekniklerinin karşılaştırmalı analizi,” Akademik Bilişim, 10-12, 2010.

Published
2019-12-12
How to Cite
[1]
C. Akyurek Anacur and R. Saracoglu, “Detecting of Warning Sounds in the Traffic using Linear Predictive Coding”, IJISAE, vol. 7, no. 4, pp. 195-200, Dec. 2019.
Section
Research Article