A novel perceptual feature set for audio emotion recognition

We present a novel system for audio emotion recognition based on the Perceptual Evaluation of Audio Quality (PEAQ) model as described by the standard, ITU-R BS.1387-1 which provides a mathematical model resembling the human auditory system. The introduced feature set performs perceptual analysis in time, spectral and Bark domains thus enabling us to represent the statistics of emotional audio for arousal and valence modes with a small number of features. Unlike the existing systems, the proposed feature set learns statistical characteristic of emotional differences hence does not require data normalization to eliminate speaker or corpus dependency. Recognition performance obtained for the well known VAM and EMO-DB corpora show that the classification accuracy achieved by the proposed feature set outperforms the reported benchmarking results particularly for valence both for natural and acted emotional data.