應用影像處理之自動樂譜辨識系統

一直以來音樂都是人類生活中不可或缺的元素，並且發展了以符號記錄聲音的五線譜。然而對大多數的人來說，閱讀五線譜並將其轉化為音樂旋律並不是一件容易的事。本研究旨在發展一套「應用影像處理之自動樂譜辨識系統」，可以自動擷取並分析印刷樂譜影像。研究方法包括：(1) 譜表分割；(2) 影像前處理；(3) 音符辨識；及 (4) 變音記號與休止符辨識。在本研究中，應用了多項影像技術 (例如：水平及垂直投影、連通元標記，樣板比對等) 以辨識五線譜中的音符、休止符及變音記號。研究結果顯示，本研究可以達到96.3%之平均音樂記號偵測率及91.7%之平均音樂記號辨識率。總結而言，本研究提出一套能夠有效辨識音符、變音記號以及休止符的自動樂譜辨識系統，能夠讓電腦閱讀樂譜中的內容並轉換為聲音資訊。同時本系統亦可結合行動裝置，作為幫助演奏者學習演奏音樂的學習工具。

關鍵字

形態學處理；樂譜辨識；影像處理；連通元標記

並列摘要

Music has always been an integral part of human’s daily lives. But, for the most people, reading a musical score and turning it into melody is not easy. This study aims to develop an Automatic music score recognition system using digital image processing, which can be used to read and analyze musical score images automatically. The technical approaches included: (1) staff region segmentation; (2) image preprocessing; (3) note recognition; and (4) accidental and rest recognition. Digital image processing techniques (e.g., horizontal/vertical projections, connected component labeling, morphological processing, template matching, etc.) were applied according to musical notes, accidentals, and rests in staff notations. Preliminary results showed that our system could achieve detection and recognition rates of 96.3% and 91.7%, respectively. In conclusion, we presented an effective automated musical score recognition system that could be integrated in a system with a media player to play music/songs given musical score images. Ultimately, this system could also be incorporated in applications for mobile devices as a learning tool, such that a music player could utilize to learn the music/songs playing.

並列關鍵字

connected component labeling ； image processing ； optical musical recognition ； morphological processing

參考文獻

[1] N. Otsu, “A Threshold Selection Method form Gray-Level Histograms,” IEEE Transactions on Systems, pp. 62-66, 1979.

[2] R.G. Casey and E. Lecolinet, “A Survey of Methods and Strategies in Character Segmentation,” IEEE Transactions on Pattern Analysis and Machine Intelligence, pp. 690-706, 1996.

[3] Cheng-Lin Liu, M. Koga and H. Fujisawa, “Lexicon-Driven Segmentation and Recognition of Handwritten Character Strings for Japanese Address Reading,” IEEE Transactions on Pattern Analysis and Machine Intelligence, pp. 1425-1437, 2002.

[4] M. Sotoodeh and F. Tajeripour, “Staff Detection and Removal Using Derivation and Connected Component Analysis,” IEEE 16th CSI International Symposium on Artificial Intelligence and Signal Processing (AISP), pp. 54-57, 2012.

[5] Chen Genfang, Zhang Liyin, Zhang Wenjun and Wang Qiuqiu, “Detecting the Staff-lines of Musical Score with Hough Transform and Mathematical Morphology,” IEEE International Conference on Multimedia Technology (ICMT) , pp. 1-4, 2010.

國際替代計量

應用影像處理之自動樂譜辨識系統

全文下載

主題瀏覽