Music Emotion Recognition 400410032 許博智 497410004 謝承諺.

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Presentation transcript:

Music Emotion Recognition 許博智謝承諺

How to Describe Emotion? ●Discrete Emotions (namely, Emotional Experience) - describe emotion state with at least 6 type behind. “happy”, “sad”, “fear”, “anger”, “surprise”, “disgust”. ●Dimensional models -Using two dimension, “valence” and “arousal”, to describe emotion.

Audio Analysis TypeFeatures DynamicsRMS energy TimbreMFCCs, spectral shape, spectral contrast HarmonyRoughness, harmonic change, key clarity, majorness RegisterChromagram, chroma centroid and deviation RhythmRhythm strength, regularity, tempo, beat histograms ArticulationEvent density attack slope, attack time. Steps to analysis music below: Music features above Subjective test Emotion recognition Music features Feature extraction Training data Emotion annotations Music features Testing dataModel training Music emotion

Machine Learning We used Support Vector Machine (SVM) to be our algorithm, due to its stable accuracy. SVM is an algorithm of separation recognition, which find optimal hyperplane of two data and depart, to judge next input. However, mess in SVM if there are 3 types in one model.

Combining Multiple Feature Combining Audio and Lyrics: Motivation from Christmas song and some emotion was conveyed by lyrics and audio. Combining Audio and Tags: Using V-A quadrant and tags to analysis Combining Audio and Images: Like audio, Images display emotion of them, too. And we can find the total result in music video.