부산대학교 도서관

Sound captured by a mobile phone's microphone is a rich source of contextual information about activity, location, and social events. In this paper, we present a robust sound classification system for recognizing the real-time context of a smartphone user. Our system can reduce unnecessary computations by discarding frames containing silence or white noise from the input audio stream in the pre-processing step. It also improves the classification performance on low energy sounds by amplifying them. Moreover, for efficient learning and application of HMM classification models, our system executes the dimension reduction and discretization on the set of multi-dimensional continuous-valued feature vectors through k-means clustering. We collected a large set of sound examples of 8 different types from daily life in a university office environment and then conducted experiments using them. Through these experiments, our system showed high classification performance.