Note Frequency Recognition and Finger Motion Capture of a Guitarist: A Survey and Challenges Ahead | ||||
| Journal of Computing and Communication | ||||
| Article 6, Volume 1, Issue 2, August 2022, Page 69-79 PDF (633.6 K) | ||||
| Document Type: Original Article | ||||
| DOI: 10.21608/jocc.2022.255009 | ||||
 View on SCiNiTO | ||||
| Authors | ||||
John Emad  ; Hager Sobeah; walaa hassan; Ahmed Serag ; Ahmed Yehia; Karim Khaled; Karim Mohamed
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| Faculty of Computer Science, Misr International University,Egypt | ||||
| Abstract | ||||
| One of the main issues that face new guitar aspirants is that there is a lot of further information, which proves a lot to take in for just a beginner as it causes much-disliked confusion. Students also face problems with their left-hand motion and correct pitch frequency. Researchers have tackled this problem in many ways and have mainly landed on using two approaches. The first was Finger Motion Capture, where previous research specialized in analyzing images and videos of the guitarist. The second was Note Frequency Recognition, where the research's primary purpose was to examine the sound and audio recorded by the guitarist. This paper surveys all the approaches taken to solve this problem, discussing them in detail and exploring the challenges faced with each approach. Furthermore, this paper proposes a hybrid solution that includes both Note Frequency Recognition and Finger Motion Capture to make a full assessment and give feedback on a guitarist's performance. | ||||
| Keywords | ||||
| Frequency Recognition; Finger Motion; Guitarist; Machine Learning | ||||
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