Automatic recognition of accessible pedestrian signals

Abstract

Accessible pedestrian signals (APS) enhance accessibility in streets around the world. Recent attempts to extend the use of APS to people with visual and audible impairments have emerged from the area of audio signal processing. Even though few authors have studied the recognition of APS by sound, comprehensive literature in Biology have been published for recognizing other simple sounds like bird and frog calls. Since these calls exhibit the same periodic and modulated nature as APS, many of the existent approaches can be adapted for this purpose. We present an algorithm that uses the mentioned approach. The algorithm was evaluated using a collection of 79 recordings gathered from streets in San José, Costa Rica, where the solution will be implemented. Three types of sounds are available: a low-pitch chirp, a high-pitch chirp and, a cuckoo-like. The results showed a precision of 87%, a specificity of 83%, a sensibility of 86%, and a F-measure of 85%.