This document summarizes meetings between students and their project guide regarding a personalized music recommendation project using a chatbot. It notes that in the first meeting they discussed the project name and base paper. In the second meeting they discussed the project presentation and changed the base paper. In the third meeting they discussed and approved the presentation. The document then continues to describe the objectives, introduction, literature review, problem statement, methodology, and other aspects of the project.

1. Guided By: Presented By:
Dhilsath Fathima. M Megha Ghosh VTU11752
B.E(CSE).,M.E(CSE) Sharmila Ghosh VTU11753
Assistant Professor Vaidehi Rahangdale VTU14431
Department of Information Technology
Major Project Review 0
Personalized Music Recommendations Using Chatbot

2. MEETING WITH GUIDE SUMMARY:
S.No Date Time Discussion Remarks
1. 17/02/2
2
9.15 am Discuss about project name,
base paper.
Project Title approved and base
paper discussion.
2. 20/02/2
2
2:30 pm Discuss about project ppt. Base paper changed and ppt
corrected.
3. 22/02/2
2
7.00 pm Discuss about project ppt. Ppt corrected and approved.
4.

3. OBJECTIVE
• The objective of our project is to determine the user's mood, and once the mood has
been identified, songs are played by the application based on the user's choice via a
chatbot.

4. INTRODUCTION:
• Chat bots are generally designed to support and scale business teams in their
relations with customers. It may exist in many major messaging programme, such
as Facebook Messenger, Slack, Telegram, Text Messages, and so on.
• The purpose of the project is to create a music-based online application that will
assist users and provide them a more personalised experience.
• This includes a chatbot that will be trained on a dataset by multiple algorithm and
open-source technologies to simulate a human conversation and select songs
based on the facial images.

5. LITERATURE REVIEW:
S.No Author Year Proposed Title Pros and Cons
1. K. Choi 2018 “A Tutorial on Deep
Learning for Music
Information
Retrieval”.
Pros: Identification of
perceptually similar audio
content.
Cons: Dataset is insufficient
to represent the music's
content.
2. K. Gurjar and Y.
Moon
2018 “A comparative
analysis of music
similarity measures
in MIR systems”.
Pros: Quick and easy
searching.
Cons: Difficulties in sound
recognition.

6. LITERATURE REVIEW(Cont):
S.No Author Year Proposed Title Pros and Cons
3. Dan Wu 2019 “Music Personalized
Recommendation
System Based on
Hybrid Filtration”.
Pros: It makes system
robust, improve performance.
Cons: Implementation is
costly.
4. J. Cleveland,
D. Cheng, M.
Zhou
2020 “Content-based
music similarity with
triplet networks”.
Pros: It received good
attention for facial recognition.
Cons: The two layers neural
network which is used to train
the network doesn’t work
efficiently.

7. PROBLEM STATEMENT:
• The recommendations on present music streaming sites are not very personalized,
and facial recognition is challenging.
• Existing systems are impersonal and rely on audio signals to determine a user's
mood, which may not accurately predict the user's current mood.
• The proposed music recommendation system uses a chatbot to evaluate user’s
current thoughts to create a personalized system that allows user’s to listen to
music based on their mood.

8. TITLE EXPLANATION
S. No Item Description
1.
Base paper title CAME: Content- and Context-Aware Music
Embedding for Recommendation.
2.
Proposed project title Personalized Music Recommendation Using
Chatbot.
3.
Improvement from existing method The expected model here creates a personalized
chatbot that predicts the user's current emotion
via facial images and allows them to listen to
music that matches their mood.

9. EXISTING SYSTEM
• A music recommender system is one that learns from a user's previous listening
experience and suggests tracks that they might enjoy hearing in the future. Existing
music recommendation algorithms are content-driven and exclusively dependent on
the characteristics of the song. This method is based on extracting and analyzing the
acoustic properties of audio signals. As a result, random recommendations were
greatly outperformed by the findings given.
• Advantages:
Based on real activity and always up-to-date.
Organizational maintenance is reduced.
• Limitations:
The system does not have enough information on the user when a new user is added.

10. PROPOSED SYSTEM
• Our proposed system is implemented as an application which can be run on the users
desktop and its main focus is to reliably determine the users mood. Human computer
interaction (HCI) has a lot of importance in todays world and the most popular concept
in HCI is recognition of emotion from facial images. In this process, the frontal view of
the facial images is utilized so as to detect the mood from the images.
• Advantages:
Chatbots have 24/7 availability.
Chatbots cause an increase in sales.
• Limitations:
Chatbots require constant maintenance.

11. EMOTION BASICS
• The facial expressions are divided into five categories: anger, joy, surprise,
sadness, and excitement.
• An emotion model is proposed that classifies a song based on any of the seven
classes of emotions viz sad, joy-anger, joy surprise, joy-excitement, joy, anger, and
sad-anger.

12. REQUIREMENTS
• Track User Emotion
• Recommend by Sorting playlist based on user’s current emotion
• Sort songs by 2 factors:
Relevancy to User Preference.
Effect on User Preference.

13. METHOLDOLOGY
• There are three major modules:
Emotion extraction module (EEM)
Audio feature extraction module(AEM)
Emotion-Audio recognition module(ERM)
•EEM and AEM are two separate modules, with the Emotion Audio Recognition
module querying the audio meta-data file to execute module mapping.
• The EEM and AEM are combined in Emotion-Audio integration module.

14. EMOTION EXTRACTION MODULE
• Done by the analysis on Images, Image of a user is captured using a webcam or it
can be accessed from the stored image in the hard disk.
• This acquired image undergoes image enhancement in the form of tone mapping
in order to restore the original contrast of the image.
• The Viola and Jones approach is used to transform it into binary picture format for
face detection (Frontal Cart property).

15. VIOLA AND JONES ALGORITHM
• The Viola-Jones object detection framework is the first object detection framework
to provide competitive object detection rates in real-time proposed in 2001 by Paul
Viola and Michael Jones.
• It can be trained to detect a variety of object classes, it was motivated primarily by
the problem of face detection.
• Here a computer needs precise instructions and constraints for the detection To
make the task more manageable, Viola-Jones requires full view frontal upright
faces.
• Thus in order to be detected, the entire face must point towards the camera and
should not be tilted to either side.
• Here we use the viola frontal cart property. The frontal cart" property only detects
the frontal face that are upright and forward facing.
• Here this property with a merging threshold of 16 is employed to carry out this
process. This value helps in coagulating the multiple boxes into a single bounding
box.
• Bounding box is the physical shape for a given object and can be used to
graphically define the physical shapes of the objects.

16. AUDIO FEATURE EXTRACTION
MODULE
• Here the list of songs forms as input audio files, and here the conversion of 16 bit
PCM mono signal around a variable sampling rate of 48.6 kHz. The conversion
process is done using Audacity technique.

17. 8 TYPES
• 1. Songs that resemble cheerfulness, energetic and playfulness are classified
under joy.
• 2. Songs that resemble very depressing are classified under the sad.
• 3. Songs that reflect mere attitude, revenge are classified under anger.
• 4. Songs with anger in playful is classified under Joy-anger category.
• 5. Songs with very depress mode and anger mood are classified under Sad-anger
category.
• 6. Songs which reflect excitement of joy is classified under Joy-excitement
category.
• 7. Songs which reflect surprise of joy is classified under Joy-surprise category.
• 8. All other songs fall under _others' category.

18. EMOTION-AUDIO RECOGNITION
MODULE
• The emotion extraction module and audio feature extraction module is finally
mapped and combined using an Emotion Audio integration module.
• The extracted for the songs are stored as a meta-data in the database. Mapping is
performed by querying the meta-data database.

19. MAPPING OF FACIALAND AUDIO
FEATURES
MODULES MAPPING

20. FEASIBILITY OF PROJECT:
• Technical Feasibility:
Anaconda.
• Economic Feasibility:
Having a real-world chatbot with whom you may communicate as if you were
communicating with a real person while listening to music chosen by the system.
• Legal Feasibility:
Creating a local open-source project and handling the errors being faced.

21. PROJECT SCHEDULING:
2
4
3
1
3
1
2
3
4
0 0.5 1 1.5 2 2.5 3 3.5 4 4.5
Problem analysis
Methodology
Design
Implementation
Verification and validation
Results and Evaluation
Report writing
Submission
Gantt Chart
May April March Feb

22. REFERENCE:
[1] B. Hu, C. Shi, J. Liu, Z. Shi, B. Goertzel and J. Feng, "Playlist recommendation based on
reinforcement learning“, 2017
[2] J. Kaitila, “A content-based music recommender system”, 2017
[3] Deger Ayata, Yusuf Yaslan and Mustafa E. Kamasak, "Emotion based music
recommendation system using wearable physiological sensors", 2018
[4] K. Choi, “A Tutorial on Deep Learning for Music Information Retrieval”, 2018
[5] K. Gurjar and Y. Moon, “A comparative analysis of music similarity measures in MIR
systems”. 2018

23. REFERENCE (Cont):
[6] F. Fessahaye et al., "T-RECSYS: A Novel Music Recommendation System Using
Deep Learning", 2019
[7] H. Immanuel James, J.James Anto Arnold, J.Maria Masilla Ruban, M. Tamilarasan
and R. Saranya, "Emotion based Music Recommendation System". 2019
[8] S. Deebika, K. A. Indira and Jesline, "A machine learning based music player by
detecting emotions“, 2019
[9] J. Cleveland, D. Cheng, M. Zhou, T. Joachims and D. Turnbull, “Content-based
music similarity with triplet networks”, 2020
[10] J. Zhang, "Movies and Pop Songs Recommendation System by Emotion
Detection through Facial Recognition“, 2020

24. THANK YOU