Emotion is a useful mean to organize music library, and automatic music emotion recognition is drawing more and more attention. Music structure information is imported to improve the result for music emotion regression. Music dataset with emotion and structure annotations is built, and features concerning lyrics, audio and midi are extracted. For each emotion dimension, regressors are built using different features on different type of segments in order to find the best segment for music emotion regression. Results show that structure information can help improve emotion regression. Verse is good for pleasure recognition, while chorus is good for arousal and dominance. The difference between verse and chorus can also help improve regressors.
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