隨著科技的進步，人類對於多媒體娛樂的需求越來越大，但對於電子音樂，一般大眾似乎無法馬上入門，因為要製作電子音樂如MIDI檔，人們一定要有一定的樂理及工具(如鍵盤)，因此，是否能幫助人們用簡單的方法來製作電子音樂，進而吸引人們對音樂的興趣，即為我們所要研究的方向。
要製作電子音樂，最普遍的方法是利用鍵盤來將旋律輸入電腦中，不但要輸入主旋律，為了要讓音樂豐富，通常還需要加入伴奏，如此對樂理不甚熟悉的人來說是一件難事。

而和絃辨識系統則僅需要使用者利用麥可風，將主旋律利用哼唱的方式輸入至電腦中，系統不但會分析出主旋律音符，還會根據主旋律來配上和絃，最後還能整合成MIDI檔將結果輸出。

如此一來，便可大大減輕使用者的負擔，使用者僅需輸入主旋律即可，既不用熟悉樂理，也不需經由繁雜的程序來輸入主旋律，使得製作電子音樂變得輕鬆。

本論文中，系統將對使用者利用麥克風所哼入的旋律進行和絃辨識。利用基本的樂理與統計方法作為理論基礎來執行和絃辨識，並將此兩種方法的分析結果利用Dynamic programming演算法找出最佳的和絃解答。如此一來，使用者不需利用鍵盤輸入，也不需有樂理基礎就可以利用辨識出的和絃結果為旋律配上伴奏。

Most people can sing or hum a song that they are familiar with. However, it is rather difficult, if not impossible, for common people without formal music skills or training to compose a song. To be able to do this, they need to use a keyboard to produce a melody, analyze its chords, and then add accompaniment via physical musical instruments or computer software. This series of actions require years of practice and experience.
In this thesis, we have constructed a system that can convert a user’s humming into music score that contains a melody track and appropriate accompaniments. First of all, a user can hum to the microphone directly and the system will do pitch tracking and note segmentation to identify music notes and measures. In the second step, the system will analyze each measure’s music note and find the chord candidates as well as associated probabilities based on music theory and statistics from a set of sample music. Finally, the system will use dynamic programming to find the best chord sequence based on chord state probabilities and chord transition probabilities. Subjective tests on the resultant music show that the automatically generated chords are satisfactory for most common users.

[1] A. Ghias, J. Logan, D. Chamberlin, and B. C. Smith. “Query by Humming Musical Information Retrieval in an Audio Database” Proc. ACM Multimedia, San Francisco, 1995.
[2] 曾明賢、王駿發,”不特定歌者卡拉OK歌曲辨識系統”,MS Thesis, National Cheng Kung University, Taiwan, R.O.C.
[3] Onishi, G., Niizeki, M., Kimura, I., Yamada, H.”A Kansei model for musical chords based on the structure of the human auditory system “IEEE International Conference on Neural Networks, 2001.
[4] Borching Su and Shyh-Kang Jeng , “Multi-Timbre Chord Classification Using Wavelet Transform And Self-Organized Map Neural Networks” , IEEE International Conference on Acoustics, Speech, and Signal Processing, 2001