Guitar chord real-time recognition system based on different classifiers and Neural Network

• Main Authors: Chih-Hung Wang, 王智弘
• Other Authors: 陳國在
• Format: Others
• Language: zh-TW
• Published: 2011
• Online Access: http://ndltd.ncl.edu.tw/handle/75096046372563021000

碩士 === 國立臺灣大學 === 工程科學及海洋工程學研究所 === 99 === In the culture for human life, music is necessary and elementary. Following the progress in science and technology, musical recognition, such as karaoke pitch-scoring machine, automatic tuning machine of music and electronic tuner, etc., did present a lot of practical values. In this study, it is to concentrate on the chord recognition, which is a part of the music-recognition technology. Thus, this study tries to use acoustic guitar chords to make in-real-time recognition by users when using user interface to match with microphone through the combination of three different recognition-classification algorithms with personal computer. Accordingly, this study at first sets up the database for the guitar chord, and further analyses the recognition percentage for three different recognition-classification algorithms as used and then makes comparison and discussion. Regarding to the experiment, two prototypes are used to perform. Between them, the first prototype uses the database of the guitar set up beforehand, which overall have ninety six items, to perform recognition percentage by using the above three different recognition -classification algorithms. The results as obtained for experiment find the averaged recognition percentages by 99.26, 94.59 and 75.46 of the guitar-chord database by using, respectively, neural network-, Naïve- Bayes classification- and Knn-algorithms are reached. As regards to the second prototype, it, respectively, makes in real time recognition percentage of guitar chord in progress by using four different chords created, respectively, by four musical instruments of different acoustic characteristics, which include two actual acoustic guitars and two virtual sound sources, and then compare the results as obtained for recognition percentages. From the result as obtained from the experiment, it can find the associated averaged recognition percentages by 80.58, 68.86 and 54.01 to the four musical instruments by, respectively, using neural network-, Naïve-Bayes classification- and Knn-algorithms are reached.