Nested iGMM recognition and multiple hypothesis tracking of moving sound sources for mobile robot audition

Abstract

The paper proposes two modules for a mobile robot audition system: 1) recognizing surrounding acoustic event, 2) tracking moving sound sources. We propose nested infinite Gaussian mixture model (iGMM) for recognizing frame based feature vectors. The main advantage is that the number of classes is allowed to increase without bound, if necessary, to represent unknown audio input. The multiple hypothesis tracking module provides time-series of separated audio stream using localized directions and recognition results at each frame. Not only for continuous sounds, the proposed tracker automatically detects appearing and disappearing point of stream from multiple hypothesis. These two modules are connected to microphone array based sound localization and separation, and the combined robot audition system achieved tracking of multiple moving sounds including intermittent sound source.