Sound event recognition in smart environments

Abstract

A rich research was reported lately on sound event recognition (SER), a particular case of audio signal classification (ASC), which in turn is part of the more general research field of auditory scene analysis (ASA). The classification of sound events in a given environment is generally more precise with fewer classes and with better knowledge of sound events expected to occur in each class. Various techniques were described in the literature which allow good performance when sound events are strictly repeating. In an effort to develop an application that in the end recognize all sound events in a given context, this work describes an application of the SER in smart environments that aims at recognizing cough sounds. Such techniques cannot rely on the strict repeatability of sound events. They must move towards recognition of sound events that are rather similar to any one of a set of established models. The main working modes we examined were to model cough as non-speech utterances and to search for a match against a database of established models.