Online Pen-Based Recognition of Music Notation with Artificial Neural Networks
Online Pen-Based Recognition of Music Notation with Artificial Neural Networks
复制标题
利用人工神经网络在线笔式乐谱识别
DOI：
10.1162/014892603322022673
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发表时间：
2003
影响因子：
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通讯作者：
Susan E. George
中科院分区：
文献类型：
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作者：
Susan E. George
计算机音乐杂志，27：2，第70-79页，2003年夏季，2003年，马萨诸塞州技术学院。基于搜索标准提供的旋律片段的音乐档案的精致搜索，或者简单地制作了thress型的安排。分析系列的样式，版权执行者可能有兴趣检测法律侵权，最常使用计算机键盘，鼠标，钢琴键盘（或其他电子仪器）进入计算机已经开发了一张印刷符号的印刷音乐的扫描设备，已开发出各种光学识别（OMR）技术机器可读格式，Blostein and Baird（1992）从那以后进行了对音乐图像分析的关键调查。 ），写一个系统，将光学扫描的音乐转换为机器可读格式。解释机制。其他研究人员都可以使用其他应用程序，例如NG（2002）数学形态在OMR中是必要的，在图像预处理后应用（即阈值，脱键和基本布局分析）。特别是，Luth（2002）进行的工作重点是对手稿的识别。跑步。此外，还探索了一般的图像处理方法，这些方法适用于印刷和手写音乐（乔治即将到来）单击“基于鼠标的范式”，“点击点”方法通常需要从菜单中选择各种音乐符号在文字处理的上下文中，工作人员是一个平行的，是单独选择字母字符，并将其放在页面上以构成句子。或草书写作），在线输入已经摆脱了“点击点击”的方法，但除了少数例外，对基于笔的基于笔的研究很明显。识别音乐符号。但是，将允许用户编写常规的音乐符号。
Computer Music Journal, 27:2, pp. 70–79, Summer 2003 2003 Massachusetts Institute of Technology. There are many different reasons why we might want to enter music notation into a computer, including editing and composing tasks, educational music theory exercises, sophisticated searching of music archives based on melodic fragments supplied as search criteria, or simply producing transposed arrangements. Musicologists may be interested in analyzing the style of a collection, and copyright enforcers may be interested in detecting legal infringements. At present, musical information is most often entered into the computer using the computer keyboard, a mouse, a piano keyboard (or some other electronic instrument) attached to the computer, or a scanning device for a sheet of printed music with offline recognition of printed symbols. Various optical musical recognition (OMR) techniques have been developed to convert scanned pages of music into a machine-readable format. Blostein and Baird (1992) presented a critical survey of problems and approaches to music image analysis. Since then, work in the OMR field has continued with researchers such as Bainbridge and Carter (1997) and Bainbridge and Wijaya (1999), who wrote a system to convert optically scanned pages of music into a machine-readable format. More recently, Droettboom and Fujinaga (2001) created an adaptive music-recognition system and interpretation mechanism. Fujinaga and Riley (2002) concentrated upon recommendations and options of file formats in the context of creating an archival image containing all relevant data extracted from a printed score. This makes interpretation of the music for archival storing, web delivery, printing, and other applications possible. Other researchers have focused upon novel process techniques, such as Ng (2002), who recognized that a stroke-based segmentation approach using mathematical morphology is necessary in OMR, applied after the image pre-processing (i.e., thresholding, de-skewing, and basic layout analysis). Similar image-processing techniques have been explored for handwritten music (Roach and Tatem 1988; Ng 2001). In particular, the work conducted by Luth (2002) focused on the recognition of handwritten music manuscripts. It is based on imageprocessing algorithms like edge detection, skeletonization, and run-length. In addition, general image-processing methods have been explored that are applicable to both printed and handwritten music (George forthcoming). With online music input, the prevailing interface technology for music editing involves the conventional ‘‘point-and-click’’ mouse-based paradigm. The ‘‘point-and-click’’ method typically requires various musical symbols to be selected from a menu and meticulously placed on a staff, and a constant movement between the menu and the staff is necessary. A parallel in the context of word processing would be to individually select the alphabetical characters and place them on the page to compose a sentence. In other fields that require some written notation (whether signatures or postcodes, mathematical notations, or cursive writing), online input has moved away from ‘‘point-and-click’’ approaches to penbased recognition. However, with few exceptions, there is a noticeable absence of research into the pen-based recognition of music symbols. One common paradigm is simply using the pen as a stylus to select music symbols from a menu bar. In another paradigm, the user must learn a special sequence of movements to enter a given music symbol. The most desirable system, however, would allow the user to write conventional music symbols.