Music Emotion Recognition Using a Variant of Recurrent Neural Network

Abstract

Searching music by emotion has always been strongly needed by users. Since music streaming applications usually have tens millions of music pieces in database, it is impossible to label emotion tags for each music piece manually. It is desired that an intelligent algorithm can recognize emotion expressed by music automatically. Valence-Arousal model is a widely used for representing emotion, but the angle of vectors on V-A plane labeled by different raters usually varies greatly, which makes it difficult to train classification models. We are trying to introduce a label space defined by pairs of antonyms, which makes emotion label relatively objective. We also used a variant model of recurrent neural network in the paper, in this model, RNN is a mean to extract features from melody, and with other features calculated by normal machine learning algorithms, this model can make a good prediction of emotions. Keywords—music emotion; harmonics and percussive; chroma recurrent neural network