Viberr is a music web application created by Titus Njiru to organize users' favorite music albums and songs. The app has three main modules - Albums, Songs, and Users. Albums allows users to create, delete, and mark albums as favorites. Songs allows users to add songs to albums, delete songs, and mark songs as favorites. Users allows creating accounts, logging in, and editing profiles. The project aimed to create a music app for the general public and included research, design, implementation, and evaluation. Areas for future improvement include adding sharing options, global song rankings, and machine learning to identify favorite songs.

1. VIBERR MUSIC APP
BY
TITUS NJIRU
(titusnjiru.dev)

2. Introduction
Viberr is a music web application that
works to address the issue of having all
favorite music albums and songs
together in an organized way. The app
has been optimized for viewing on
different screen sizes to enhance user
experience.

3. Objectives
The major objective of this project was to develop a
music app that enhances a user’s music experience.
The system is broken down into the following
modules:
1. Albums – allow users to create albums, delete or
mark them as favorite.
2. Songs – Allow user to add songs to albums, delete
them and mark as favorite.
3. Users – This module allows users to create
accounts, login, view & edit their profiles.

4. Scope
• This project target was to create an application for the general
public.
• The project include research, design and prototyping, system
implementation as well as report to describe the final
product.
• The project was tried by a few young people to evaluate its
acceptability.
• The project doesn’t include sharing of music among different
users or other ranking features.
• The major project constraints were tight schedules and lack of
utmost expertise in the chosen development languages.

5. Reccommendation
Based on users feedback the following
areas of improvement were identified:
1. Implemenation of sharing music option
2. Global ranking of songs
3. Inclusion of machine learning
techniques to identify user’s favorite
songs.