Classification of breath and snore sounds using audio data recorded with smartphones in the home environment

In this paper, classification between snore-inhale (SI), snore-exhale (SE), breathe-inhale (BI), breathe-exhale (BE) and noise (NS) sounds is performed. The database is obtained from 7 subjects, who recorded whole night audio data in their private home environments with their own smartphones. Preprocessing is done by a modification of an adaptive noise suppression method [1]. The classification system consists of 5 binary RobustBoost classifiers (RBs) [2] applying the one-vs.-rest strategy and an artificial neural network (ANN) for voting on the outputs. ReliefF and Sequential Forward Selection (SFS) extract a 5-dimensional feature vector, consisting of psychoacoustic features from time and spectral domain. Sensitivity (Se) and specificity (Sp) in percent on a preprocessed (i.e. the signal contains only sound activity segments), representative 1 h 20 min dataset are: Sesi, sE, Bi, BE, NS = {80.91, 80.01 34,12, 66.45, 29.53} Spi, SE, Bi, BE, NS = {83.56, 91.70, 90.53, 83.32, 93.51}.