Identification of sound sources in soundscape using acoustic, psychoacoustic, and music parameters

Abstract

This paper explores the possibility of automatic identification/classification of environmental sounds, by analyzing sound with a number of acoustic, psychoacoustic, and music parameters, including loudness, pitch, timbre, and rhythm. The sound recordings of single sound sources labeled in four categories, i.e., water, wind, birdsongs, and urban sounds including street music, mechanical sounds and traffic noise, are automatically identified with machine learning and mathematic models, including artificial neural networks and discriminant functions, based on the results of the psychoacoustic/music measures. The accuracies of the identification are above 90% for all the four categories. Moreover, based on these techniques, identification of construction noise sources from general urban background noise is explored, using the large construction project of London Bridge Station redevelopment as a case study site.