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We propose a neural-network training algorithm that is robust to data imbalance in classification. In our proposed algorithm, weights are introduced to training examples, effectively modifying the trajectory traversed in the parameter space during the learning process. Furthermore, the proposed algorithm would reduce to the normal stochastic gradient decent learning if the data is balanced. On the...
A complete emotional expression in natural face-to-face conversation typically contains a complex temporal course. In this paper, we propose a temporal course modeling-based error weighted cross-correlation model (TCM-EWCCM) for speech emotion recognition. In TCM-EWCCM, a TCM-based cross-correlation model (CCM) is first used to not only model the temporal evolution of the extracted acoustic and prosodie...
In the music information retrieval (MIR) research, developing a computational model that comprehends the affective content of music signal and utilizes such a model to organize music collections have been an essential topic. Emotion perception in music is in nature subjective. Consequently, building a general emotion recognition system that performs equally well for every user could be insufficient...
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