Detecting of warning sounds in the traffic using linear predictive coding method

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 thesis. 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 Predictive 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 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.