Auditory context classification using random forests

High-level semantic information can be extracted from audio materials to facilitate various content-based analysis and context-awareness applications. In this paper, we propose a novel automatic auditory context classification method, which combines the characterization of audio events and the inference of auditory context category in a single ensemble analysis framework. In the proposed framework, key audio events in the context are characterized by composite features from discriminative representation models (local discriminant bases, pseudo-semantic and bag-of-audio-words) learned from samples. A random forest based ensemble learning and classification model is employed for auditory contexts, in which individual segments of audio stream are classified and aggregated by Hough voting or bagging to form the final context category. The effectiveness of the proposed approach is demonstrated by the experimental results.