Bachelor of Information Systems (Hons) Information Systems Engineering Faculty of Information and Communication Technology (Kampar Campus), UTAR. I understand that the University will upload soft copy of my final year project/dissertation/thesis* in pdf format in UTAR Institutional Repository, which can be made accessible to UTAR community and public. In addition to activity recognition model, we also implement a content-based music recommender system using the Spotify API, where this recommender should recommend a list of tracks to users based on the user preferences such as favorite artist, song and activity predicted from the activity recognition model.

Bachelor of Information Systems (Honours) Information Systems Engineering Faculty of Information and Communication Technology (Kampar Campus), UTAR.

LIST OF TABLES

LIST OF ABBREVIATIONS

Introduction

So in this project, it will build a music recommender by improving the contextual awareness of the user recommendation system so that it can provide the most suitable songs to the users. In this recommendation system, one of the inputs uses the Human Activities Recognition (HAR), which means that the system will predict the movement of the users based on the sensor data and then predict which numbers will be more appropriate. Moreover, since this project is a combination of human activity recognition and the hybrid filtering system, it can be a recommender system that is much closer to humans and also understands us compared to the traditional recommender system.

It is one of the neural networks commonly used for image processing, classification and also recognition.

Figure 1-1 Content-Based Approach Example

Literature Review

It provided streaming service to the users but also had the services as well as music recommendation features in this application. As Figure 2-2 showed above, it is the interface of the Apple music, and it contains a recommendation to customize the selection of music for the users based on their listening habits. As Figure 2-10 showed above, it's the Tidal interface, and it also includes recommendations for the latest listening patterns as well.

31] But when it comes to the activity, as if we are playing a piano, and what kind of music genre should we refer to.

System Methodology

Tools and Technologies Used
HAR Model

Overall Architecture Flow
Standard Evaluation Metrics for Model Performance
Dataset Collection
Data Pre-processing

Music Recommender System 1. Overall Architecture Flow

Spotify API
A/B Testing

Implement Issues and Challenges
Timeline

The next steps are to extract the frame of the video from the data set, then proceed with formatting and transforming as well. Based on the second part of the proposed design in Figure 3.1, the very first step before using the Spotify API, the user may need to authenticate their Spotify account to the system. Finally, the web browsers allowed us to test the presentation of the system result.

The figure 3-2 above shows the overview of the selected architecture for this human activity recognition model using the CNN-LSTM network. First, starting from the input images that split the frame of the video, it proceeds to the CNN for the feature extraction using the VGG 16. After building the model, the evaluation of the model is a must, so that we can prove the accuracy of the trained model.

In this project, the activity recognition model must be derived from the classification results, so we will evaluate the performance of the model using evaluation metrics that cover several types of results, such as accuracy, precision, recall, confusion matrix, and also f1 scores. In addition, the classes of the video dataset are also a problem, which means that it is difficult to determine that one dataset contains all the custom classes that we want to build in this human activity recognition model. In addition, the training accuracy for this human activity recognition model is not accurate enough, which means that it may take more time to find hyperparameter settings or adjust the training dataset.

Nevertheless, there is also a possibility that the size of the data set, since in this activity recognition model more than two classes will be classified, so it may require more sample data to avoid the unbalanced class problem. In addition, there are also some of the challenges while implementing Music Recommender System.

System Implementation

Steps to Setup and Build the Project 1. Human Activity Model

Music Recommendation System Step 1: Spotify Developer Account
Fully Connected Both Module

To summarize this section, in this project there are 9 classes that we used for the human. In this architecture, it has 2 parts for the training, for the first part which belongs to the CNN, also known as VGG16, it is used to extract the feature. Furthermore, for the second part, the LSTM which is our main focus is used to train the model by keeping the useful memory and helping us to get the higher accuracy.

In order to improve the accuracy of the model trained, it is necessary to perform grid search for the optimization of the hyperparameter. For example, Figure 4-4 below shows the video that will be predicted with the model. There are few options for us to choose for the authentication method whether we want direct access for the services (Client Credentials) or verify before using the services (Implicit Grant).

While in this project we will use the combination for both this authentication, it is because we need to access the services for the recommendation service and also the figure x below shows the client information flow as the user directly requests the access token without authenticating with their Spotify account . For the figure x below shows the implicit grant flow that the user will be asked to approve in order to get access token permission to request the Spotify services. After getting access permission to request the Spotify services, it's time to play with its services.

Figure 4-13 below shows that the MRS have been imported and are applicable to the main system. Bachelor of Information Systems (Honours) Information Systems Engineering Faculty of Information and Communication Technology (Kampar Campus), UTAR.. After searching and accessing the URL link in any web browser, the user will enter the Spotify authentication page as the figure 4-16 below shows, only the users who have authentication will go to the next page, which is the main page to get 4-17).

System Evaluation And Discussion

The table above shows that the ten sample tracks for both recommended tracks, where the purpose of this test is to count the number of unique tracks of the suggested music, compared to the original Spotify recommended tracks. As the figures below show that the evidence / references of the recommended songs for each class. The table above shows that the comparison between the existing playlist and the proposed playlist, which is the purpose of this testing to compare which example, if a user X uses the existing playlist and he/she cycles for about 36 minutes, but when they use the suggested playlist and he/she is able to cycle for about 50 minutes which is longer than the existing playlist.

As can be seen from the bar chart above, it shows that the suggested playlist always allows the user to stay on the activity longer than the other activity, which means it can give users more. 42] Before performing this evaluation, test the object while running a version of the object in a controlled environment. In this testing, the total number of participants is 30 participants and from these 30 participants we will randomly select our sample data to perform statistical analysis.

As observed in Figure 5-2 and Figure 5-3 above, it shows that the result of distribution for the statistical tests for these A/B tests. First, the average rating in step 3 shows that the control has an average speed of 3.20 over 5, while the average speed of the test group is 3.83 over 5. To round off this test result, we can say that the alternative hypothesis (H1) is accepted while the null hypothesis is rejected because the p value is below the significant level which is 0.05.

Lack of proper quality processing of data sets (video), as the data sets come from different sources, so they can be different from each other, and also video/image resolution is also an issue the biggest one that needs to be handled in a proper manner. The parameters of the current CRNN model are not suitable for the current training data sets, the most suitable parameters should be adjusted and selected before training the model, so that the model can be trained efficiently.

Figure 5-3 Sample Detection 1 of Computer Work Class

Conclusion

Project Review
Future Work

Available: https://towardsdatascience.com/illustrated-guide-to-lstms-and-gru-s-a-step-by-step-explanation-44e9eb85bf21. Available: https://play.google.com/store/apps/details?id=deezer.android.app&gl=US. Available: https://study.com/learn/lesson/what-is-music-characteristics-of-music-how-music-is-.

Available: https://www.usjcycles.com/news/pros-and-cons-of-listening-to-music-while-cycling/. Available: https://www.rocknsoulcafe.com/music-genres-you-should-combine-with-your-meals/#:~:text=You%20should%20play%20Indie%20music,that%20meal%20so % 20% too much.

FINAL YEAR PROJECT WEEKLY REPORT

WORK DONE
WORK TO BE DONE
PROBLEMS ENCOUNTERED - No
SELF EVALUATION OF THE PROGRESS - Still on track
SELF EVALUATION OF THE PROGRESS - Still on track for the progress
PROBLEMS ENCOUNTERED
WORK TO BE DONE - Complete report

PROGRESS SELF-ASSESSMENT - Still on track for progress - Still on track for progress. Limited time to retrain Human Activity Recognition model for accuracy improvement, therefore accuracy remains Project 1. Required originality parameters and UTAR approved limits are as follows:. i) Overall Similarity Index is 20% and below, and. ii) Matching of individual sources listed must be less than 3% each, and (iii) Matching texts in continuous block must not exceed 8 words.

Note The supervisor/candidate(s) must provide the Faculty/Institute with an electronic copy of the complete originality report set. Based on the above results, I declare that I am satisfied with the originality of the final year project report submitted by my students as mentioned above. Form Title: Supervisor Comments on Originality Report Generated by Turnitin for Final Year Project Report Submission (for Undergraduate Programs).

UNIVERSITI TUNKU ABDUL RAHMAN