Project Based Learning-II

(Guidelines and Work Book)

Course Code: 217533
(2019 Course)
Second Year Engineering
Year 2023 - 2024

Group No: 12
Team Members:
1. Pravin Pradip Kavthale
2. Akshay Yogesh Chhallare
3. Bhushan Ramdas Avhad
4. Om Balkrushna Waghchawre

Project Title:
Emotion based music recommendation system

Name of Mentor:
Mrs. S.K. Deore

1
 DEPARTMENT OF ARTIFICIAL INTELLIGENCE AND DATA
SCIENCEENGINEERING

MATOSHRI COLLEGE OF ENGINEERING AND RESEARCH

CENTRE, EKLAHARE NASHIK 422105 SAVITRIBAI PHULE
PUNE UNIVERSITY
2023 -2024

A PRELIMINARY REPORT ON

“Emotion Based Music Recommendation System”

SUBMITTED TO THE SAVITRIBAI PHULE PUNE UNIVERSITY,

PUNE IN THE PARTIAL FULFILLMENT OF THE REQUIREMENTS
FOR ACADEMIC OF SECOND YEAR OF AI&DS ENGINEERING

SUBMITTED BY

Pravin Pradip Kawthale Roll No: 72

Akshay Yogesh Challare Roll No: 71
Om Balkrushna Waghchawre Roll No: 67
Bhushan Ramdas Avhad Roll No: 69

2
 CERTIFICATE
This is to certify that the project report entitles
“Emotion Based Music Recommendation System”
Submitted By
Pravin Pradip Kavthale Roll No: 72
Akshay Yogesh Challare Roll No: 71
Bhushan Ramdas Avhad Roll No: 69
Om Balkrushna Waghchawre Roll No: 67
is a bonafide student of this institute and the work has been carried out by them
under the supervision of Prof. S.K. Deore and it is approved for the partial
fulfillment of the requirement of Savitribai Phule Pune University, for the award
of the second year degree of Artificial Intelligence and Data Science Engineering.

Mrs. S.K. Deore Dr. J. J. Chopade

Guide HOD of AI&DS Engineering

Dr.G.K. Kharate
Principal

Department of AI&DS Engineering

Matoshri collage of Engineering and Research Centre

Place: Nashik
Date : / / 2024

3
 ACKNOWLEDGEMENT

First and foremost, we would like to thank to our guide of this project,
Prof. S.K. Deore for the valuable guidance and advice. He inspired us greatly to
work in this project. His willingness to motivate us contributed tremendously to
our project. We also would like to thanks for showing us some example that
related to the topic of our project.
Apart from our efforts, the success of any project depends largely on the
encouragement and guidelines of many others. So, we take this opportunity to
express our gratitude to Prof. J. J. Chopade, Head of Department of Artificial
Intelligence and Data Science Engineering, Matoshri College of Engineering and
Research Centre, Nashik who have been instrumental in the successful
completion of this project.
The guidance and support received from all the members who contributed
and who are contributing to this project, was vital for the success of the project. I
am grateful for their constant

Pravin Kawthale
Akshay Challare
Bhushan Avhad
Om Waghchawre
(S.E. AI&DS ENGG)

Place: Nashik
Date: / / 2024

4
 Table of Contents

Sr. No. Description Page No.

Preamble 6

1 Project Based Learning Syllabus 8

2 Recommended Guidelines and Phases 10

3 Evaluation and Continuous Assessment 11

Sheet
4 Project Information Sheet 12

5 Project Monitoring (1 sheet per week) 18onwards

5
Preamble
Project-based learning is an instructional approach designed to give students the opportunity
to develop knowledge and skills through engaging projects set around challenges and problems
they may face in the real world. PBL is more than just projects. With PBL students "investigate
and respond to an authentic, engaging, and complex problem, or challenge" with deep and
sustained attention. PBL is "learning by doing." The truth is, many in education are recognizing
we live in a modern world sustained and advanced through the successful completion of
projects. In short, if students are prepared for success in life, we need to prepare them for a
project-based world. It is a style of active learning and inquiry-based learning. (Reference:
Wikipedia). Project based learning will also redefine the role of teacher as mentor in learning
process. Along with communicating knowledge to students, often in a lecture setting, the
teacher will also to act as an initiator and facilitator in the collaborative process of knowledge
transfer and development. The PBL model focuses the student on a big open-ended question,
challenge, or problem to research and respond to and/or solve. It Brings what students should
academically know, understand, and be able to do and requires students to present their
problems, research process, methods, and results.
Project based learning (PBL) requires regular mentoring by faculty throughout the semester for
successful completion of the idea/project tasks selected by the students per batch. For the
faculty involved in PBL, teaching workload of 4 Hrs.’/week/batch needs to be considered. The
Batch should be divided into sub-groups of 4 to 5 students. Idea implementation/Real life
problem/Complex assignments / activities / projects under project based learning is to be
carried throughout semester and Credit for PBL has to be awarded on the basis of internal
continuous assessment and evaluation at the end of semester.

6
 Abstract
In the era of digital music streaming services, personalized recommendations play a crucial
role in enhancing user experience and engagement. Traditional music recommendation systems
primarily rely on user listening history, preferences, and demographic information. However,
these systems often overlook the emotional context of music consumption, which significantly
influences user satisfaction and engagement.

This project proposes an Emotion-Based Music Recommendation System (EMRS) that

leverages machine learning techniques to recommend music songs based on the user's
emotions. The system utilizes emotion recognition algorithms to analyze user input, such as
text or speech, and extract emotional features. These features are then mapped to a predefined
emotion model, enabling the system to understand the user's emotional state.

The recommendation engine incorporates a hybrid approach, combining collaborative filtering

techniques with content-based filtering and emotion modeling. Collaborative filtering
considers user-item interactions and similarities among users, while content-based filtering
analyzes music features such as genre, tempo, and lyrics. The emotion modeling component
enhances the system's ability to recommend music that aligns with the user's current emotional
state.

Key components of the EMRS include data preprocessing for emotion extraction, machine
learning models for emotion classification, and recommendation algorithms for personalized
music suggestions. The system also incorporates user feedback mechanisms to continuously
refine and improve recommendation accuracy over time.

The evaluation of the EMRS involves user studies and comparative analyses with existing
music recommendation systems. Metrics such as recommendation accuracy, user satisfaction,
and engagement levels are assessed to validate the effectiveness of the proposed approach.

The outcome of this project is a scalable and adaptive Emotion-Based Music Recommendation
System capable of providing personalized music recommendations that resonate with users'
emotional states. The system's ability to understand and respond to user emotions not only
enhances music discovery but also fosters deeper engagement and satisfaction within the music
streaming experience.

7
Project Based Learning Syllabus:
Course Objectives:
1. To develop critical thinking and problem-solving ability by exploring and proposing
solutions to realistic/social problem.
2. To evaluate alternative approaches, and justify the use of selected tools and methods.
3. To emphasizes learning activities that are long-term, inter-disciplinary and student.
4. To engages students in rich and authentic learning experiences.
5. To provide every student the opportunity to get involved either individually or as a
group so as to develop team skills and learn professionalism.
6. To develop an ecosystem that promotes entrepreneurship and research culture among
the students.
Course Outcomes:
CO1: Identify the real-life problem from societal need point of view.
CO2: Choose and compare alternative approaches to select most feasible one
CO3: Analyze and synthesize the identified problem from technological perspective CO4:
Design the reliable and scalable solution to meet challenges
CO5: Evaluate the solution based on the criteria specified
CO6: Inculcate long life learning attitude towards the societal problems Group Structure:
Working in supervisor/mentor monitored groups; the students plan, manage, and complete
a task /project/activity which addresses the stated problem.
1. There should be team/group of 4-5 students
2. A supervisor/mentor teacher assigned to individual groups

Selection of Project/Problem:
The problem-based project-oriented model for learning is recommended. The model begins
with the identifying of a problem, often growing out of a question or “wondering”. This
formulated problem then stands as the starting point for learning. Students design and
analyze the problem within an articulated interdisciplinary or subject frame. A problem can
be theoretical, practical, social, technical, symbolic, cultural and/or scientific and grows
out of students’ wondering within different disciplines and professional environments. A
chosen problem has to be exemplary. The problem may involve an interdisciplinary
approach in both the analysis and solving phases. By exemplarity, a problem needs to refer
back to a particular practical, scientific, social and/or technical domain. The problem should
stand as one specific example or manifestation of more general learning outcomes related
to knowledge and/or modes of inquiry. There are no commonly shared criteria for what
constitutes an acceptable project. Projects vary greatly in the depth of the questions
explored, the clarity of the learning goals, the content and structure of the activity.
 A few hands-on activities that may or may not be multidisciplinary
 Use of technology in meaningful ways to help them investigate, collaborate, analyze,
synthesize and present their learning.
 Activities may include-Solving real life problem, investigation /study and Writing
report

Assessment:
The institution/head/mentor is committed to assessing and evaluating both student
performance and program effectiveness. Progress of PBL is monitored regularly on weekly
basis. Weekly review of the work is necessary. During process of monitoring and continuous
assessment AND evaluation the individual and team performance is to be measured. PBL is

8
monitored and continuous assessment is done by supervisor/mentor and authorities. Students
must maintain an institutional culture of authentic collaboration, self-motivation, and peer-
learning and personal responsibility. The institution/department should support students in this
regard through guidance/orientation programs and the provision of appropriate resources and
services. Supervisor/mentor and Students must actively participate in assessment and
evaluation processes. Group may demonstrate their knowledge and skills by developing a
public product and/or report and/or presentation.
 Individual assessment for each
 Group assessment
 Documentation and presentation

Evaluation and Continuous Assessment:

It is recommended that all activities should to be recorded regularly, regular assessment of
work need to be done and proper documents need to be maintained at college end by both
students as well as mentor (PBL work book). Continuous Assessment Sheet (CAS) is to be
maintained by all mentors/department and institutes.
Recommended parameters for assessment/evaluation and weightage:
1. Idea Inception and Awareness /Consideration of -Environment/ Social /Ethics (10%)
2. Outcomes of PBL/ Problem Solving Skills/ Solution provided/ Final product
(Individual assessment and team assessment) (40%)
3. Documentation Gathering requirements, design and modelling,
implementation/execution, use of technology and final report, other documents) (15%)
4. Demonstration (Presentation, User Interface, Usability) (20%)
5. Contest Participation/ publication (15%)
6. PBL workbook will serve the purpose and facilitate the job of students, mentor and
project coordinator. This workbook will reflect accountability, punctuality, technical
writing ability and work flow of the work undertaken.
References:
 De Graff E, Olmos A., and red.: Management of change: Implementation of problem
based and project-based learning in engineering. Rotterdam: Sense Publishers. 2007.
 Gopala, “Project Management core textbook”, 2 Indian Edition.
 James Shore and Shane warden, “The art of Agile Development”.
 www.schoology.com
 www.howstuffworks.com

9
Recommended Guidelines and Phases:
PBL is learning through activity. One of the teachers can be appointed as coordinator for PBL.
Following are the recommended guidelines that will work as an initiator and facilitator in
process of completion of PBL.
1. In first week of commencement of 2nd semester or preferably at the end of first
semester let the coordinator create awareness about PBL (what, why, and how) among
the students. Convey students expected outcomes, assessment process and evaluation
criteria.
2. Get groups of students registered preferably 4-6 students per group.
3. Assign mentor to each group.
4. Provide guidelines for title identification (Problem can be some real life situation that
needs technology solutions. This situation can be identified by meeting people around,
visiting various industries, society, and institutes. The solution can be prototype, model,
convertible solutions, survey and analysis, simulation, and similar).
5. Let students submit the problem identified in prescribed format(Title, Problem
statement, details of a problem undertaken, and what is need of solution to the problem)
6. Coordinator and mentor can approve the problem statements based on feasibility and
learning outcomes expected for first year engineering students
7. Mentor is to monitor progress of the task during phases of project work. Broadly phases
may include- requirements gathering, preparing a solution, technology design for the
solution. (optional phases- implementation and testing)
8. Weekly monitoring and continuous assessment record is to be maintained by mentor.
9. Get the report submitted at the end of semester.

10
Evaluation and Assessment Sheet (To be filled in my mentor)

Sr. No. Details Maximum Marks Marks Obtained

Problem Identification
1. 10
(Idea Inception)
Problem Analysis
2. 15
(Requirement Gathering)
Proposed Solution Model/Design/
3. 20
Process / prototype

4. Technology Solution Model 15

5. Expected Outcomes 05

6. Implementation and Testing 10

Regularity (Attendance + Weekly

7. 10
Progress Reporting)
Awareness /Consideration of -
8. Environment/ Social /Ethics/ Safety 05
measures/Legal aspects

9. Contest Participation/ publication 05

10. Report 05

Total Marks 100

Date:

Name & Sign of Mentor

11
Project Information Sheet
Project 03
ID
Title Emotion based music recommending system

Problem To develop the web site that will suggest songs based on the emotions of the user
Statement
Name Prof. S.K. Deore
of
Mentor
Group Division Roll Name Mobile Email ID
Members No. Numbe
r
72 Pravin Kawthale kavthalepravin@gmail.
8055217077 com
71 Akshay Challare 7719811915 akshaychhallare009@g
mail.com
69 Bhushan Avhad 9322746906 bhushanachad1312@g
mail.com
67 Om Waghchawre 8055806854 omwaghchawre02@gm
ail.com

12
Project Monitoring / Progress Information Sheets:
Week 1
Date:
Current Work Phase of Project:

Discussions Held:

Progress till Date:

Remarks:

Sign of Mentor:

13
Week 2
Date:
Current Work Phase of Project:

Discussions Held:

Progress till Date:

Remarks:

Sign of Mentor:

14
Week 3
Date:
Current Work Phase of Project:

Discussions Held:

Progress till Date:

Remarks:

Sign of Mentor:

15
Week 4
Date:
Current Work Phase of Project:

Discussions Held:

Progress till Date:

Remarks:

Sign of Mentor:

16
Week 5
Date:
Current Work Phase of Project:

Discussions Held:

Progress till Date:

Remarks:

Sign of Mentor:

17
 Index
Sr. No. Content Page No
1 INTRODUCTION 19
1.1 Introduction 19

1.2 Problem Statement 19

1.3 Objectives 19

1.4 Scope of the Project 19

2 LITERATURE REVIEW 20

3 ARCHITECTURAL DESIGN 21

3.1 Architectural Design 21

3.2 Use case diagram 22

4 System architecture 23

4.1 Flowchart 23

4.3 Hardware And Software Requirements 2

4.5 Actual Methodology 24

5 RESULTS 25

6 ADVANTAGES, DISADVANTAGES AND APPLICATIONS 26

6.1 Advantages 26

6.2 Disadvantages 26

6.3 Applications 26

7 TESTING 27

7 CONCLUSION 31
8 REFERENCES 32

18
 CHAPTER-1
INTRODUCTION AND ANALYSIS

1.1 Introduction
The Emotion-Based Music Recommendation System (EMRS) revolutionizes music
discovery by incorporating users' emotional states into personalized music recommendations.
Traditional systems overlook emotions, relying solely on historical data and preferences.
EMRS analyses emotional cues from user inputs using machine learning, enhancing
recommendation accuracy. By combining collaborative and content-based filtering with
emotion modelling, EMRS aims to deliver highly tailored music suggestions. The project
evaluates EMRS through user studies to validate its effectiveness in enhancing music discovery
and user satisfaction.

1.2 Problem Statement

Traditional music recommendation systems overlook users' emotional states,
relying solely on historical data and preferences. This leads to suboptimal recommendations
that fail to align with users' current emotions, reducing engagement and satisfaction.
Developing an Emotion-Based Music Recommendation System (EMRS) is crucial to
accurately recognize emotions from user inputs, integrate emotion modelling with
recommendation techniques, and improve music discovery and user satisfaction in real-time
contexts.

1.3 Objective
The project aims to:
1. Develop an Emotion-Based Music Recommendation System (EMRS) integrating users'
emotional states into recommendations.
2. Implement emotion recognition algorithms for analysing user inputs (text or speech) to
extract emotional features.
3. Utilize machine learning models for emotion classification and mapping to a predefined
emotional model.
4. Combine collaborative filtering and content-based filtering techniques in the
recommendation engine for personalized music suggestions.
5. Evaluate EMRS through user studies to measure recommendation accuracy, user
satisfaction, and engagement levels.
6. Refine and improve EMRS iteratively based on user feedback to enhance effectiveness
over time.

1.4 Scope of the Project

The project scope includes developing emotion recognition algorithms from user
inputs (text/speech) and integrating machine learning models for emotion classification. Data
pre-processing tasks will extract relevant features for collaborative filtering, content-based
filtering, and emotion modelling in the recommendation engine. Designing an intuitive user
interface for emotional input and feedback is crucial. Evaluation metrics will assess
recommendation accuracy and user satisfaction, ensuring scalability and adherence to ethical
considerations such as privacy and algorithm fairness.

19
 CHAPTER-2
LITREATURE AND REVIEW

2.1 Literature Review:

1.1 Emotion-Based Music Recommendation Systems:

Research in the field of music recommendation systems has evolved from traditional
collaborative filtering and content-based methods to incorporating user emotions. Studies
by authors such as Li et al. (2017) and Kim et al. (2019) have explored emotion-aware
music recommendation systems using machine learning and sentiment analysis techniques.
1.2 Facial Emotion Recognition and AI:
Advancements in facial emotion recognition using AI, particularly deep learning
models like convolutional neural networks (CNNs) and facial landmark detection
algorithms, have enabled accurate emotion detection from facial expressions. Works by
authors such as Ekman and Friesen (1978) laid foundational theories in facial expressions
of emotions, while recent studies by Zhang et al. (2020) and Kim et al. (2021) showcase
the efficacy of AI-driven emotion recognition systems.
1.3 Integration of Emotion Recognition in Music Recommendation:
Recent literature has started to explore the integration of facial emotion recognition
with music recommendation systems. Studies by Wang et al. (2020) and Lee et al. (2022)
have proposed frameworks that leverage AI-driven emotion recognition from facial
expressions to tailor music recommendations based on users' emotional states in real-time.
1.4 Challenges and Opportunities:
Despite advancements, challenges such as real-time processing of facial emotions, user
privacy concerns, and diverse emotional contexts in music listening remain areas of
ongoing research. Opportunities lie in refining AI models for emotion recognition,
integrating multimodal data sources (facial, audio, textual), and developing user-centric
music recommendation interfaces.

2.2 Project Review:

The project aims to bridge the gap between facial emotion recognition using AI and
personalized music recommendation systems. By leveraging deep learning models for emotion
detection from facial expressions and integrating this data into a music recommendation
engine, the project seeks to enhance user experience and engagement in music streaming
platforms.

The proposed system involves real-time processing of facial emotions, mapping

detected emotions to predefined emotional models, and generating music recommendations
aligned with users' emotional states. The integration of AI-driven emotion recognition
techniques with music recommendation algorithms presents a novel approach to personalized
content delivery.

Challenges such as model accuracy, computational efficiency for real-time processing,

and ensuring user privacy must be carefully addressed in the project implementation.
Collaborative efforts between AI researchers, music experts, and user experience designers are
crucial for developing an effective and user-friendly system.

20
 CHAPTER-3
ARCHITECTURAL DESIGN

3.1 Architecture Diagram:

Fig 3.1 Architecture diagram

 User Interaction: This is the part of the app that users directly interact with. It likely
includes features for browsing music, creating playlists, and providing feedback on
recommendations.
 User Input: This layer captures the user’s actions and selections within the app. This
might include things like liked songs, playlists created, and searches performed.
 Emotion Detection Module: This component is likely not typical for most music
streaming apps. It’s possible this system is designed to analyze user input to detect the
user’s emotional state and tailor music recommendations accordingly.
 Feature Extraction (Audio Analysis): This layer analyzes the audio properties of the
music in the app’s library. This might include tempo, rhythm, and genre.
 Recommendation Engine: This is the heart of the music recommendation system. It
uses the data about user input (layer 2) and music features (layer 4) to recommend music
to users.
 Music Repository: This is the database that stores the music that the app can
recommend to users.

21
3.2 Use Case Diagram:

Fig 3.2 Use case Diagram

User:

 The user can enter the name of a singer and the system will add them to the system.
 The user can enter a language and the system will update the Al module.
 The user can capture video of themselves, which presumably the system analyzes to
extract their emotions.
 Based on the user's captured emotions, the system recommends songs to the user.

Administrator:

 The administrator can edit the emotions that the system recognizes.
 There are also include directives for capturing video and adding emotions, but it's not
clear from this diagram how the administrator would use these functionalities.

22
 CHAPTER-4
SYSTEM ARCHITECTURE

FLOWCHART:

Fig 4.1 Flow chart

HARDWARE AND SOFTWARE REQUIREMENTS:

1. Hardware

 Processor (CPU): A mid-range to high-end processor with decent processing power is

likely needed, especially if the emotion recognition engine is complex.
 Memory (RAM): The RAM requirement would depend on the chosen software but
typically 4GB or more would be recommended.
 Storage: Enough storage space to store the music library and the emotion recognition
model.
 Other: A microphone or camera might be needed to capture user input for emotion
detection (depending on the system design).

23
2. Software

 Operating System (OS): A common operating system like Windows, macOS, or

Android would likely suffice.
 Music Player Application (You Tube): The core music player software.
 Additional Software: Libraries or frameworks supporting the music player
functionalities and potentially the emotion recognition engine.
 Python: Python library version 3.11

ACTUAL METHODOLOGY:

1. Emotion Recognition

 Data Acquisition: The system captures user input, which could be through:
o Facial expressions: A camera captures the user's face, and software analyzes
facial features to detect emotions. This might involve deep learning algorithms
and pre-trained models.
 Emotion Classification: The captured data (facial expressions, voice, etc.) is fed into
a pre-trained emotion recognition model. This model is designed to classify the user's
emotional state into categories like happy, sad, angry, etc.

2. Music Recommendation

 Music Emotion Tagging: A separate process analyzes the music library beforehand.
Each song is assigned emotional tags based on its musical features like tempo, rhythm,
and melody, or through manual human annotation.
 Matching Emotions: Once the user's emotion is detected, the system recommends
songs with matching emotional tags. For example, if the user feels happy, the system
might recommend upbeat and energetic songs.

3. Music Player Functionality

 User Interface: The music player provides a user interface for interacting with the
system. This might include options for manual song selection alongside emotion-based
recommendations.
 Music Playback: The core functionality of playing music files is integrated with the
emotion recognition and recommendation features.

24
CHAPTER-5
RESULT

25
 CHAPTER-6
ADVANTAGES, DISADVANTAGES AND APPLICATION

Advantages:
1. Personalized Experience: AI algorithms can analyse user preferences and emotional
states to provide personalized music recommendations tailored to individual tastes and
moods.
2. Enhanced User Engagement: By recommending music that aligns with the user's
current emotional state, the system can help users discover new songs or artists that
resonate with their feelings, leading to increased engagement and satisfaction.
3. Improved Music Discovery: Emotion-based recommendation systems can help users
explore a wider range of music genres and styles based on their mood, facilitating
serendipitous discoveries and expanding their musical horizons.
4. Contextual Relevance: By considering contextual factors such as time of day,
location, and activity, the system can recommend music that is appropriate for specific
situations, enhancing the overall listening experience.
5. Adaptability: AI-powered recommendation systems can continuously learn and adapt
to changes in user preferences and emotional states over time, ensuring that
recommendations remain relevant and up-to-date.

Disadvantages:
1. Subjectivity: Emotions are subjective and can vary widely between individuals,
making it challenging to accurately predict and recommend music based solely on
emotional cues.
2. Limited Understanding of Context: AI algorithms may struggle to interpret complex
contextual cues, such as sarcasm, irony, or cultural nuances, which can impact the
accuracy of recommendations.
3. Privacy Concerns: Emotion-based recommendation systems rely on collecting and
analysing user data, raising privacy concerns related to data security and user consent.
4. Overreliance on Data: The effectiveness of AI algorithms depends heavily on the
quantity and quality of available data, which may be limited or biased, leading to
suboptimal recommendations.
5. Algorithmic Bias: AI algorithms can inadvertently perpetuate biases present in the
training data, resulting in recommendations that Favor certain music genres, artists, or
demographics over others.

Applications:
1. Music Streaming Platforms: Emotion-based recommendation systems can be
integrated into music streaming platforms like Spotify, Apple Music, or YouTube
Music to enhance the user experience and increase user engagement.
2. Online Radio Stations: AI-powered recommendation systems can be used to create
personalized playlists or radio stations based on the user's current mood or emotional
state.
3. Fitness and Wellness Apps: Emotion-based music recommendation systems can be
incorporated into fitness and wellness apps to provide users with motivational playlists
tailored to their energy level or workout intensity.
4. Retail and Hospitality: Emotion-based music recommendation systems can be
deployed in retail stores, restaurants, or hotels to create atmosphere-enhancing playlists
that resonate with customers' emotions and preferences.

26
 CHPTER-7
TESTING

Project Name: Emotion-Based Music Recommendation System

Version: 1.0
Date of Testing: [12 Apr 2024]
Tested By: [S.K Deore]
Tested Environment: VS Code

1. Introduction
This document outlines the testing process and results for the Emotion-Based Music
Recommendation System. The system aims to recommend music based on user-input
emotions, utilizing AI algorithms for accurate recommendations.

2. Testing Objectives
 Evaluate the functionality and accuracy of emotion-based music recommendations.
 Assess system performance under varying loads and environments.
 Ensure compatibility across devices and browsers.
 Verify data privacy and security measures.
 Gather user feedback to gauge usability and satisfaction.

3. Testing Methodology
The testing followed a structured approach covering functional, performance, compatibility,
security, usability, regression, and exploratory testing techniques. Automated testing tools were
used for efficiency and accuracy.

4. Test Cases
4.1 Functional Testing
User Interface Testing
 Description: Verify the usability of the system’s interface for inputting emotions.
 Test Case: Attempt to input various emotions using the provided interface.
 Expected Result: Emotions should be accurately captured and processed by the
system.

27
Input Validation Testing
 Description: Validate that the system handles invalid or unexpected inputs
appropriately.
 Test Case: Input non-emotion words or symbols and check system response.
 Expected Result: System should provide an error message or handle invalid inputs
gracefully.

Recommendation Accuracy Testing

 Description: Evaluate the accuracy of music recommendations based on input
emotions.
 Test Case: Input specific emotions and verify recommended music genres or tracks.
 Expected Result: Recommended music should align with the specified emotions
accurately.

4.2 Performance Testing

Load Testing
 Description: Measure system performance under various user load conditions.
 Test Case: Simulate multiple users accessing the system simultaneously.
 Expected Result: System response time should remain within acceptable limits under
load.

Response Time Testing

 Description: Measure the time taken by the system to process inputs and generate
recommendations.
 Test Case: Record response times for different emotion inputs.
 Expected Result: System should provide recommendations promptly within expected
response times.

4.3 Compatibility Testing

Device Compatibility
 Description: Test the system’s compatibility across different devices (e.g., desktop,
mobile).
 Test Case: Access the system using various devices and screen sizes.
 Expected Result: System should adapt and display correctly across different devices.

Browser Compatibility
 Description: Verify the system’s compatibility with different web browsers.
 Test Case: Access the system using popular web browsers (e.g., Chrome, Firefox, and
Safari).
 Expected Result: System should function correctly across supported browsers without
layout or functionality issues.

28
4.4 Security Testing
Data Privacy Testing
 Description: Ensure user data (emotions, preferences) is handled securely and
privately.
 Test Case: Monitor data transmission and storage practices during system use.
 Expected Result: User data should be encrypted during transmission and stored
securely.

Input Validation
 Description: Test the system for potential vulnerabilities such as SQL injection or
cross-site scripting attacks.
 Test Case: Attempt to inject malicious scripts or SQL queries through input fields.
 Expected Result: System should detect and prevent malicious inputs, maintaining data
integrity and system security.

4.5 Usability Testing

User Feedback
 Description: Gather feedback from users on system usability and effectiveness in
music recommendations.
 Test Case: Conduct surveys or interviews with users after using the system.
 Expected Result: Positive feedback regarding ease of use and accurate music
recommendations based on emotions.

4.6 Regression Testing

Test Suites
 Description: Develop and run regression test suites to ensure new updates or changes
do not break existing functionality.
 Test Case: Execute regression tests on core system functionalities after updates.
 Expected Result: Existing functionalities should remain stable and unchanged post-
update.

Integration Testing
 Description: Test the integration of AI algorithms with the music recommendation
system after any updates or modifications.
 Test Case: Verify AI algorithm accuracy and performance in generating music
recommendations.
 Expected Result: AI algorithms should consistently provide accurate and relevant
music recommendations.

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4.7 Exploratory Testing
Edge Cases
 Description: Test the system with extreme inputs or edge cases to identify unexpected
behaviours.
 Test Case: Input rare or extreme emotions and assess system response.
 Expected Result: System should handle edge cases gracefully without errors or
unexpected behaviours.

Random Testing
 Description: Perform random testing to explore different paths and inputs within the
system.
 Test Case: Input random emotions or sequences to test system response variability.
 Expected Result: System should maintain stability and provide accurate
recommendations across random inputs.


5. Test Results and Analysis

Provide a detailed analysis of test results, including issues encountered, bugs identified, and
areas of improvement based on the test cases and their descriptions.

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 CONCLUSION

The development of an emotion-based music recommendation system leveraging AI

technology presents both significant opportunities and challenges. By harnessing the power of
machine learning algorithms, such a system can offer personalized music recommendations
tailored to users' emotional states, enhancing their listening experience and engagement with
music content. The advantages of such a system include its ability to provide personalized
recommendations, improve music discovery, and adapt to changes in user preferences over
time. Moreover, its potential applications span across various industries, including music
streaming platforms, fitness apps, retail, and hospitality.

However, it's essential to acknowledge the limitations and challenges associated with building
and deploying such systems. These include the subjective nature of emotions, the difficulty in
interpreting complex contextual cues, privacy concerns related to data collection and analysis,
and the risk of algorithmic bias. Addressing these challenges will require careful consideration
of ethical and privacy implications, ongoing refinement of AI algorithms, and transparent
communication with users regarding data usage and recommendations.

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 REFERENCES

JOURNALS

1. Journal of Artificial Intelligence Research

2. IEEE Transactions on Affective Computing
3. ACM Transactions on Interactive Intelligent Systems
4. International Journal of Human-Computer Studies
5. Journal of Music Information Retrieval
6. ACM Transactions on Intelligent Systems and Technology
7. Frontiers in Psychology - section on Music Psychology
8. Journal of New Music Research
9. International Journal of Semantic Computing
10. Journal of Machine Learning Research - section on Machine Learning for Music
Discovery

REFFERENCE PAPER:

1. Yang, Y., & Yang, Y. H. (2017). "Music Emotion Recognition: A State-of-the-Art

Review." IEEE Transactions on Affective Computing, 8(4), 494-515.
2. Lee, J. H., Lee, J. H., & Yoo, H. J. (2018). "Deep Learning-Based Music Mood
Classification." ACM Transactions on Multimedia Computing, Communications, and
Applications, 14(1), 1-21.
3. Li, Y., et al. (2019). "Emotion-Based Music Recommendation Using Deep Learning."
Proceedings of the International Conference on Multimedia Retrieval, 115-123.
4. Zhu, C., & Yang, Y. H. (2020). "A Survey of Emotion Recognition in Music." Frontiers
in Psychology, 11, 1-14.

WEBSITES

1. https://how2electronics.com/?p=5472
2. https://cityosair.readme.io/docs/esp8266nodemcu
3. https://app.cpcbccr.com/AQI_India/
4. https://youtu.be/dsjgQL4nu8

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