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Affective content analysis of music emotion through EEG

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Abstract

Emotion recognition of music objects is a promising and important research issues in the field of music information retrieval. Usually, music emotion recognition could be considered as a training/classification problem. However, even given a benchmark (a training data with ground truth) and using effective classification algorithms, music emotion recognition remains a challenging problem. Most previous relevant work focuses only on acoustic music content without considering individual difference (i.e., personalization issues). In addition, assessment of emotions is usually self-reported (e.g., emotion tags) which might introduce inaccuracy and inconsistency. Electroencephalography (EEG) is a non-invasive brain-machine interface which allows external machines to sense neurophysiological signals from the brain without surgery. Such unintrusive EEG signals, captured from the central nervous system, have been utilized for exploring emotions. This paper proposes an evidence-based and personalized model for music emotion recognition. In the training phase for model construction and personalized adaption, based on the IADS (the International Affective Digitized Sound system, a set of acoustic emotional stimuli for experimental investigations of emotion and attention), we construct two predictive and generic models \(AN\!N_1\) (“EEG recordings of standardized group vs. emotions”) and \(AN\!N_2\) (“music audio content vs. emotion”). Both models are trained by an artificial neural network. We then collect a subject’s EEG recordings when listening the selected IADS samples, and apply the \(AN\!N_1\) to determine the subject’s emotion vector. With the generic model and the corresponding individual differences, we construct the personalized model H by the projective transformation. In the testing phase, given a music object, the processing steps are: (1) to extract features from the music audio content, (2) to apply \(AN\!N_2\) to calculate the vector in the arousal-valence emotion space, and (3) to apply the transformation matrix H to determine the personalized emotion vector. Moreover, with respect to a moderate music object, we apply a sliding window on the music object to obtain a sequence of personalized emotion vectors, in which those predicted vectors will be fitted and organized as an emotion trail for revealing dynamics in the affective content of music object. Experimental results suggest the proposed approach is effective.

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Acknowledgements

This research was supported by the National Science Council under Contract Nos. MOST-104-2221-E-030-015-MY2 and NSC-102-2218-E-030-002. The authors would like to thank Dr. Yuan-Pin Lin (Swartz Center for Computational Neuroscience, Institute for Neural Computation, University of California, San Diego) for sharing neuroscience insights related to emotion recognition, inspiring research ideas, and shaping our future directions.

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Authors and Affiliations

  1. Department of Computer Science and Information Engineering, Fu Jen Catholic University, New Taipei City, Taiwan (R.O.C.)

    Jia-Lien Hsu, Yan-Lin Zhen & Tzu-Chieh Lin

  2. Department of Psychology, Fu Jen Catholic University, New Taipei City, Taiwan (R.O.C.)

    Yi-Shiuan Chiu

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  1. Jia-Lien Hsu
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Correspondence to Jia-Lien Hsu.

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Communicated by B. Prabhakaran.

This research was supported by the National Science Council under Contract No. MOST-104-2221-E-030-015-MY2 and NSC-102-2218-E-030-002.

Appendix

Appendix

The Four Seasons is an example of program music (i.e., music with a narrative element). Each of the four sonnets is expressed in a concerto, which in turn is divided into three phrases or ideas, reflected in the three movements (fast-slow-fast) of each concerto. The arrangement is as follows:

  • Vivaldi Concerto No. 1 in E major, Op.8, RV269, “La primavera” (Spring):

figure a

Setting the mood of the opening movement, the opening ritornello (recurrent phrase) is marked in the score “The spring has returned.” The first violin solo is marked “Song of the birds,” while after a return of the ritornello, comes a soft murmuring on the violin. After the next ritornello comes the lightning and thunder, followed by an extensive return to the singing birds and gaiety.

The slow movement is a musical description of the snoozing goatherd, watched over by his dog, whose bark is imitated throughout the movement on the violas with repeated notes to be played “very loud and abruptly”.

The third movement, a rustic dance, opens with a suggestion of rustic bagpipes, complete with an imitation of their drones by sustained notes on the low strings [53].

  • Vivaldi Concerto No. 4 in F minor, Op.8, RV297, “L’inverno” (Winter):

figure b

The strings, with trills in the violins, describe the shivering in the winter cold. Swift arpeggios and scales by the solo violin describe the whipping of the wind, while a series of abrupt chords suggest stamping feet and running to get warm. But rapid tremolos show that all this activity is useless, since the teeth continue to chatter. Violin pizzicati depict the falling raindrops, after which a warm melody on the solo violin describes the pleasant indoors with its roaring fire. The Finale opens with sliding phrases by the violin—walking and slipping on thin ice. The orchestra joins with a slower rhythm to indicate the hesitant steps and fear of falling. But then we are back indoors, enjoying the warmth while the winds howl outside [53].

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Hsu, JL., Zhen, YL., Lin, TC. et al. Affective content analysis of music emotion through EEG. Multimedia Systems 24, 195–210 (2018). https://doi.org/10.1007/s00530-017-0542-0

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