Information Fusion of Audio Emotion Recognition Based on Kernel Entropy Component Analysis in Canonical Correlation Space

Kernel Entropy Component Analysis(KECA), an effective information fusion tool, is realized using descriptor of information entropy and optimized by entropy estimation. However, it merely put the information or data from different channels together to achieve the information fusion without considering their intrinsic structures and relations. In this paper, we enhance the performance of KECA by introducing KECA in Canonical Correlation Space (CCS) or KECA+CCS. Not only the intrinsic structures and relations are considered in CCS, but also the nature of input data are revealed by entropy estimation. It improves the recognition accuracy effectively. The effectiveness of the proposed method is evaluated through experimentation on two audio-based emotion databases. The results show that the proposed method outperforms the existing methods based on similar principles.