## 2 Related Work
### Physiology-Enhanced MIR Systems  
As physiological changes are an inherent part of emotion [6], with the rapid development of wearable technology, physiological signals are of great potential for enhancing the state-of-art of MER. However, studies exploiting physiological signals for MER are still scarce, with the following being pioneering exceptions. [PERSON] et al. 's Affective DJ [27] was an early study on personalizing music selection for individual listeners based on their physiological responses to music. Specifically, the study inferred users' emotional state from their galvanic skin response (GSR) and automatically generated a relaxing or energizing playlist based on the inferred arousal level of the user. Their evaluation revealed a significant correlation between participants' arousal levels and skin conductance. Following up, [PERSON] et al. proposed another affective music player named AMP and probed the relationship between skin temperature and valence [28] as well as that between skin conductance and arousal [29]. Validation of the AMP found that decreasing skin temperature was correlated with more positive emotion induced by music. A few studies also tried to incorporate other types of physiological signals into MIR systems as well such as electrocardiogram (ECG) [30], photoplethysmography (PPG) [31], and electroencephalography (EEG) [32]. For instance, [PERSON] et al. [30] designed a music selection system which used heart rate variability (HRV) to infer arousal status of users such as sleep, boredom, anxiety, and panic and performed music selection based on a set of prespecified rules to maintain users' arousal at a moderate level. More recently, [PERSON] et al. [31] proposed a more sophisticated framework for emotion-aware music recommendation where features extracted from listeners' galvanic skin response (GSR) and photoplethysmography (PPG) signals were used to predict their emotional status (i.e., positive vs. negative valence; high vs. low arousal). [PERSON] et al. [33] also explored the relationships between music-induced emotion and physiological signals of listeners and found some features extracted from heart rate (HR) and electrodermal activity (EDA) differed significantly across emotion categories. Notwithstanding the contributions of these existing studies, the investigation on exploiting physiological signals for emotion-aware MIR is still limited. One limiting factor is the lack of publicly available datasets. To date, the most widely used dataset on this topic is DEAP [34], which contains EEG and physiological signals of 32 participants recorded while they watched 40 pieces of music video. However, the stimuli in DEAP were not exactly music but music videos which included a large amount of dynamic visual information. It is not possible to separate the effect of auditory input using DEAP. The PMEmo dataset [35], on the other hand, used music as the stimuli, specifically, 794 music chorus clips annotated by 457 subjects. While PMEmo contains moment-to-moment emotion annotations, it only includes one type of physiological signals, electrodermal activity (EDA). More recently, a new dataset named MUSEC [36] was constructed with EEG signals of 20 listeners when they listened to 220 pieces of music. While the study [36] collected listeners' ratings on valence and arousal, these emotion labels were not released with the dataset. Thus, a dataset with multiple physiological signals, corresponding music stimuli, and emotion labels is much in need. Another research gap lies in insufficient work on features extracted from physiological signals. Most studies exploited the time-series nature of physiological signals and extracted features in the time-domain (e.g., means and standard deviations) and frequency-domain (e.g., power spectrum) [37]. Other studies employed deep neural network machine learning methods to automatically generated features [31], which suffers from the \"black box problem\" in interpreting meanings of the features.