初學者民謠吉他學習系統

Beginner Acoustic Guitar Learning System

張瑞元(Ruei-Yuan Jhang)

指導教授 ： 陳恆佑

共同指導教授 ： 張克寧(Keh-Ning Chang)

國立暨南國際大學/科技學院/資訊工程學系/碩士(2015年)

摘要

民謠吉他在許多人的認知上算是容易學習的樂器，然而初學時期仍需要克服許多難關，例如和弦指法、吉他音色、吉他譜示意等等。初學者通常不會發現這些問題，往往需要老師的適時提醒來修正；然而自主練習時老師並不在身邊，初學者若彈錯而不自知，練習效果將事倍功半，間接打擊初學者的信心。

因此本論文開發一套民謠吉他學習系統，從吉他基礎音階練習開始，加上和弦指法練習，最後搭配歌曲簡易彈奏，循序漸進的吉他學習法幫助初學者練習，建立吉他的基本知識。系統不僅能提供完整的教材，電腦搭配麥克風收音也能檢視初學者彈奏的正確性，並即時給予回饋，讓初學者可以在第一時間調整自己的練習狀況。教材的設計上也加入遊戲模式來計分，擺脫單調的練習框架，讓學習充滿樂趣。

關鍵字

和弦指法練習 ； 音階練習 ； 吉他學習法

並列摘要

People find that the acoustic guitar is easy to learn. But beginners still need time to overcome many challenges, such as chord fingering, guitar tone, guitar tabs schematic, and so on. Beginners often do not beware of these problems and teachers have to remind and correct students. However, teachers are not always available when beginners practice guitar. Practicing without error correcting is of limited use.
This thesis attempts to develop an acoustic guitar learning system, including practicing of scale, chord and songs. Beginners can choose from the three practices what they want to learn. In order to detect the pitches from beginners’ playing, Harmonic Product Spectrum algorithm is employed to identify real-time pitches which are then used to recognize chords.
The system adopts a step by step approach. In order to motivate beginners to practice, every part of practice is presented in some interesting way, like random pitch learning, chord game, pop music accompaniment, and so on. When the boring practice becomes interesting, beginners can not only actively try to practice, but also enjoy the fun of guitar.

並列關鍵字

Chord Fingering Practicing ； Scale Practicing ； Guitar Learning

參考文獻

參考文獻

一、英文部分

[Burns 06] Burns, A.-M., Wanderley, M.M., “Visual Methods for the Retrieval of Guitarist Fingering,“ International Conference on New Interfaces for Musical Expression (NIME06), Paris, France, 2006

[Huang 08] Huang, K., Do, E.Y.-L. and Srarner, T., “PianTouch: A wearable haptic piano instructionsystem for passive learning of piano skills,” IEEE Symposium on Wearable Computers, pp. 41-44, 2008

[Kerd 07] Kerdvibulvech, C., Saito, H., “Vision-Based Detection of Guitar Players’ Fingertips Without Markers,” Computer Graphics, Imaging and Visualisation, pp. 419-428, 2007