Prediction of the Distribution of Perceived Music Emotions Using Discrete Samples

• Published in: IEEE transactions on audio, speech, and language processing Vol. 19; no. 7; pp. 2184 - 2196
• Main Authors: Yi-Hsuan Yang, Chen, H. H.
• Format: Journal Article
• Language: English
• Published: Piscataway, NJ IEEE 01.09.2011; Institute of Electrical and Electronics Engineers
• Subjects: Accuracy; Applied sciences; Arousal; Clips; Computational modeling; emotion distribution prediction; Emotions; Exact sciences and technology; Feature extraction; Information, signal and communications theory; Kernel; Machine learning; Mathematical models; Miscellaneous; Music; music emotion recognition; Planes; Predictive models; regression; Samples; Signal processing; Statistical analysis; Statistical methods; subjectivity; Telecommunications and information theory; Training; valence; Performance evaluation; emotion distribution prediction; Emotion recognition; subjectivity; Probabilistic approach; Probability distribution; valence; Algorithm; Modeling; Sound recognition; Learning; Calling line identification presentation; regression; Arousal; Automatic recognition; Musical sound; music emotion recognition

Typically, a machine learning model of automatic music emotion recognition is trained to learn the relationship between music features and perceived emotion values. However, simply assigning an emotion value to a clip in the training phase does not work well because the perceived emotion of a clip v...