… time-consuming than linear approaches. The neural networks have strong internal fittability
… layer does not process the information; it only distributes the input vectors to the hidden layer…

… It is not possible to separate the effect of auditory input using … Other studies employed deep
neural network machine … role in the predictive modeling of music-induced emotion, we trained …

… in automatic audio emotion recognition relies on characteristics of the input, output, … neural
networks to recognize induced emotions, with the ultimate goal being to classify similar music …

… proven effects on neural networks in the brain. Using other … predicts the emotion of the
human-based on physiological … for emotion recognition, when multiple inputs (ie, physiological …

… s theory, emotional experience is a response to physiological … and genetic algorithm to
predict emotional states by the EEG … the Recurrent Neural Network (RNN) to automatic emotions …

… physiological evidences for the music e®ects on human … the basic physic quantities (inputs)
of music, then we attempt to … predictive power, which performed much better than linear PLS …

… ) to predict the music emotions for the music and the music … One is to get physiological emotion
metrics directly from the users… Each network consists of an input layer, hidden layers, and …

… In this figure, it is possible to observe the input neurons, … later train independent prediction
models for both dimensions (… designed dimensional prediction system using neural networks, …

… There is a piece of 30-s music between every two videos to … (d) training the ML models and
predicting the emotion class. For our … emotion recognition based on multi-modal physiological …

… only physiological features were used as the model input, … that the physiological features do
not have a purely linear … such as deep neural networks [45] and recurrent neural networks [39…