# Emotion-Driven Music and IoT Devices for Collaborative Exer-Games
## 5 Music Recommendation and Personalization Based on Emotions
### Experimentation
#### 5.6.1 Perceived Emotions by the Listeners  
The goal of the first experiment is to corroborate the affective annotations of songs with the emotions that users feel when listening to them. In the design of the experiment, we used a repository of _Spotify_ songs that were annotated with the RIADA emotion recognition models as a part of a previous research project.  
Instruments: On the one hand, a playlist was created from a repository of songs. It consists of 16 _Spotify_ songs, 4 songs from each of [PERSON]'s quadrants. The main criteria for selecting these songs was that their affective annotations were characterized by having one dominant emotion, i.e., songs that could evoke more than one emotion with high probability were excluded. The preferences or tastes of the participants in the experiment were not taken into account in the selection of the songs, and the songs were randomly ordered. On the other hand, a Google survey form was programmed to gather the participants' responses. The survey asked each participant to introduce the emotion that they felt when listening to each song. In the design of the survey, the \"Pick-A-Mood\" (PAM) model [52] was used. PAM is a cartoon-based pictorial instrument for representing the user's possible emotional states based on [PERSON]'s affective model. More specifically, PAM expresses nine emotional states, two for each of the four quadrants and a neutral state. This visual representation reduces the time and efforts of the respondents, which makes the PAM model suitable for the design of these state-based emotions.  
Methodology: The participants were in a relaxed environment and wearing headphones to listen to the playlist. The experiment consisted of playing each song and asking the participant what they felt while listening to it. Between songs, the listener had enough time to introduce the answer on the online survey. The duration of the experiment was approximately 50 min.  
Results: Table 2 presents the main outcomes of this experiment. The rows represent the affective annotations of the songs included in the playlist and the columns show the listeners' responses. For example, the component [Sad, Happy] represents the percentage of responses (6.25%) in which a listener declared to feel happy when listening to a sad song. Therefore, the diagonal of the table indicates the percentage of responses that matched the affective annotations of the songs. Overall, a high percentage of aggressive, happy, and relaxed songs were correctly recognized by the listeners (72.92%, 77.08%, and 89.58%, respectively). However, the percentage of sad songs recognized correctly was lower (64.58%), as many were identified as relaxed (both emotional categories share a similar valence).  
Conclusions and improvements: The number of participants in the experiment is small, and as such, the results should be interpreted with caution. Nevertheless, these preliminary results are promising and suggest that the accuracy of the affective annotations is good with respect the emotions felt by the listeners. We acknowledge that the duration and conditions of the experiment limit its scalability. The participants must be relaxed and concentrated in the listening, avoiding interruptions or early termination of the activity. As a future  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Annotation/Responses** & **Aggressive** & **Happy** & **Sad** & **Relaxed** \\ \hline Aggressive & 72.92 & 20.83 & 6.25 & 0 \\ Happy & 10.42 & 77.08 & 10.42 & 2.08 \\ Sad & 6.25 & 6.25 & 64.58 & 22.92 \\ Relaxed & 2.08 & 2.08 & 6.25 & 89.58 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Affective annotations versus emotions perceived by the listeners.  
improvement, an alternative method for making a large-scale validation of annotations should be designed. Ideally, this validation should be automated, which requires a database of songs emotionally labelled by users. These labels should have similar semantics to those used in this work in order to ensure the validity of results.