Automatic guitar music transcription
Written music or music transcriptions are useful mediums of music for learning and sharing, which usually come in the form of musical score, which are generally the standard transcription format, and the tablature format, which are focused on the guitar. Creating music transcription however can be q...
Saved in:
| Main Author: | |
|---|---|
| Format: | text |
| Language: | English |
| Published: |
Animo Repository
2011
|
| Subjects: | |
| Online Access: | https://animorepository.dlsu.edu.ph/etd_masteral/4010 https://animorepository.dlsu.edu.ph/cgi/viewcontent.cgi?article=10848&context=etd_masteral |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Institution: | De La Salle University |
| Language: | English |
| Summary: | Written music or music transcriptions are useful mediums of music for learning and sharing, which usually come in the form of musical score, which are generally the standard transcription format, and the tablature format, which are focused on the guitar. Creating music transcription however can be quite a tedious pro- cess. Though various systems exist to aid in creating transcribed music, almost all generate musical scores, lacking a focus for the guitarists community. This re- search aims to develop a system that will help in automatically generating guitar tablatures and musical scores based on musical audio data. Information gathered from the audio consist of pitch, onsets and durations, chords, and beat and tempo. Major issues that were encountered during the research were harmonics for pitch detection, thresholding for onset detection, chord distinction, similar chord struc- tures for chord labeling, and the subjective quality of tempo. Results are generally acceptable, performed on a data set that contains 22 files with varying elements. 70% accuracy was gathered from pitch detection, 60% accuracy from onset de- tection, 86% accuracy for chord distinction, 85% accuracy for chord labeling, and 81% accuracy for beat and tempo. |
|---|