Proposal
SRS
SDD
Thesis

Team Members

Ahmed Serag

Team Leader

Ahmed Yehia

Team Member

Karim Mohamed

Team Member

Karim Khaled

Team Member

John Emad

Team Member

Supervisors

Walaa Hassan

Associate Professor

Hager Sobeah

TA

Abstract

There are many self taught guitarists that view tutorials online in order to learn new songs. Beginning guitarists, however, find it difficult to execute the right technique. Playing guitar focuses mostly on left hand movement as it controls the tunes. Our Project aims to correct new guitarists techniques for their left hand by scanning their hand’s movement and correcting their finger positions. The proposed solution also captures the frequency of each played note and then produces feedback which would help players to adjust their techniques, making sure they are playing everything correctly and not just copying people playing online. The finger recognition would be done through the MediaPipe a computer vision framework which detects body motion and classifies objects, which can run on most devices. we will also use predictive and classification models to assess the guitarist’s performance.

System Objectives

  • To have a solid solution for hand tracking across the guitar fret board by mid december.
  • To increase the accuracy of our algorithm by utilizing the usage of sound validating if users played the right note with the right finger by the end of sprint 3.
  • To establish a set of exercises based on expert guitarists opinions and assessing how new guitarists played basic scales and chords by the end of sprint 4.
  • To build an ETL pipeline which takes the guitarist’s footage and extracts the notes they played with the corresponding finger positions by the end of sprint 5.
  • To provide an analysis on the guitarist’s finger movement by the end of sprint 5.
  • To build a predictive model for the best hand positions in certain music scales and chords by the end of sprint 6.

System Scope

  • Extract the hand and finger positions from a given guitar video performance.
  • Extract the single notes played from the the audio of the guitarists performance.
  • Extract the chords played from a guitarist using MLP.
  • Provide music exercises for the user to try out
  • Give real-time feedback for the hand motion correction and whether or not the note or chord were played correctly in the exercise.
  • Provide a feedback report on the total notes hit correctly and methods on how to correct the guitarists hand movements.

Documents and Presentations

Proposal

You will find here the documents and presentation for our proposal.

Document

Presentation

SRS

You will find here the documents and presentation for our SRS.

Document

presentation

SDD

You will find here the documents and presentation for our SDD.

Document

presentation

Thesis

You will find here the documents and presentation for our Thesis

Document

Presentation

Accomplishments

Publications

Competitions

Paper Title

Competition Title

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