## 5 Experimental Results  
For demonstrating the function of the EPMRS, we developed an Android-based application named MusicAI. MusicAI is built by using the Python programming language. The DCNN approach is developed by the Keras module of the Python programming language. MusicAI is an end-user APP installed in the smart phone device for providing users' online music listening. First, to trace the individual user listen behavior, we request all users to register an account once they open MusicAI at the first time. There are two ways to register a valid account in MusicAI: through the formal registration approach or through the user's Facebook account. Figure 4a shows a screenshot of the  
Figure 3: (a) the mel-spectrogram of an audio signal for a song with the _Downtempo_ genre; (b) the mel-spectrogram of an audio signal for a song with the _Rock_ genre. user's registration for MusicAI. These registration details are stored as a part of the user-data in the database. A user should provide the user credentials to login into the MusicAI application as shown in Figure 4b. After successful user login, the MusicAI asks the user to provide his current emotion as shown in Figure 5a. In MusicAI, we consider four user emotions: happy, normal, sad, and surprise. It is compulsory for the user to select one of the four user emotions before using any functionality of the MusicAI. By adding this constraint, we assume that a user will provide a _true_ user's emotional state. Once the user selects his current emotion, the MusicAI displays a list of songs to the user for listening as shown in Figure 6a. We emphasize that users can change their current emotions at any time later if they want to change the recommended songs based on their emotions. The list of songs is a collection of songs that is stored on user's device and the database of the MusicAI. The MusicAI application monitors the user behavior such as his current listening history, the genre of the selected song, the user's click stream, etc. The user information including his current emotion can be seen and shown in Figure 5b. The MusicAI application sends the user's data to the EPMRS for analysis. The EPMRS analyzes the user's data to generate a list of recommended songs. The MusicAI application displays the list of recommended songs received from the EPMRS to the user as shown in Figure 6b. We use the MusicAI to trace the user's information (e.g., the emotion, the location, the time of play, etc.) and monitor the listen history of the user. The MusicAI sends all data collected from the user to the EPMRS for deep learning. Based on the built DCNN model and the collected data from users, the EPMRS recommends songs to the user. Figure 4: (a) registration form a user to get the user details; (b) user’s login page. Figure 5: (**a**) list of emotions where a user can select his current emotion; (**b**) user information. Figure 6: (**a**) music list available on the user’s device and the MusicAI’s server; (**b**) music recommendations’ list based on the user’s information and his current listening history of songs. In EMPRS, we measure the performance in terms of the accuracy of the songs' recommendation with respect to different genres. Therefore, we propose the accuracy formula, \(A_{u,s}\), of recommending a song \(s\) to a user \(u\) as  
\[A_{u,s}=\frac{\sum\text{the number of correct song recommendations}}{\sum\text{the total number of song recommendations}}\times 100. \tag{11}\]  
In order to compare the performance of the EPMRS, we adopt two baseline systems: the content similarity for music recommendation system (CSMRS) [25] and the personalized music recommendation system based on electroencephalography feedback (PMRSE) [26]. The CSMRS uses a CB recommendation approach to extract the latent features from the audio content of the song. The CSMRS extracts the MFCCs from the audio signal and aggregates them into a bag-of-words representation. The PMRSE uses the K-MeansH clustering algorithm for clustering the music based on the content presented in the music.