智能音乐辅助系统：乐谱获取与学习的创新方案

设计和实现基于语音识别与编辑的自动作曲系统,旨在解决视觉障碍者和音乐初学者在乐谱访问和音乐理解方面的困难。研究内容包括音符识别、音符编辑和多种文件生成。通过音符识别算法和语音技术的集成,用户的音频输入可以转换为乐谱中的音符和时值。音符编辑功能允许用户灵活地修改音符的音高。系统能够生成多种文件格式,例如,图片、音频、可编辑版乐谱和盲文文件,以满足用户的展示和编辑需求。该研究对音乐辅助技术的发展具有重要意义,为视觉障碍者和音乐初学者提供了创新的学习工具和音乐创作方式。未来,自动作曲系统有望进一步发展,提高算法准确性、用户体验和创作自由度。同时,与其他领域技术的融合将进一步扩展其功能和应用场景,推动音乐辅助技术的进一步发展。     

乐谱图像倾角快速检测方法

计算机光学乐谱识别技术是将传统的纸质型乐谱转化为计算机能够“读懂”的数字音乐，在计算机音乐领域中具有重要的应用价值、乐谱识别系统的输入是乐谱扫描图像，而扫描过程中出现的图像倾斜现象，会给识别过程中的谱线定位和谱段切割带来诸多困难，必须对图像作有效的倾斜校正以保证系统的性能。为此，提出了一种快速的乐谱图像倾角检测方法。该方法首先利用乐谱文档的自身结构特点，对图像进行预处理，滤除乐谱图像中不具备方向性的干扰像素，然后通过多组图像水平投影队列间的交叉相关性计算对倾角进行检测。其特点是在确保检测倾角精度的同时具有非常高的执行效率。实验结果表明这一方法是有效、实用的。     

基于OMR技术的乐谱图像匹配算法

提出了一种数字乐谱图像识别与匹配方法，采用光学乐谱识别(OMR)技术识别数字乐谱图像建立对应的MIDI文件，利用一一对应算法进行匹配，根据相似度来判断同一音乐作品的不同数字版本的乐谱图像，并在理论上就此算法的有效性进行了论证。同时，仿真实验结果表明这一方法能有效地实现对不同数字版本的同一音乐作品的乐谱图像的识别、归类与校正。     

基于残差门控循环卷积和注意力机制的端到端光学乐谱识别方法

光学乐谱识别对推动音乐智能化与数字化有着重大意义。传统的乐谱识别流程冗杂,易导致错误积累,但目前基于序列建模的乐谱识别方法不能从全尺度上获取音符上下文信息,在识别效果上仍有提升空间。为此,提出一种基于残差门控循环卷积和注意力机制的端到端光学乐谱识别方法。以残差门控循环卷积作为骨干网络,丰富模型提取上下文信息能力;结合一个注意力机制解码器,能更好地挖掘乐谱特征信息及其内部相关性,增强模型表征能力并对乐谱图像中的音符及音符序列进行识别。实验结果表明,改进后的网络与原卷积循环神经网络（CRNN）模型相比,符号错误率和序列错误率均显著下降。     

浅谈多媒体技术在音乐教学中的运用

计算机音乐这个极富时代感的新词汇，它对从事电脑工作的人和搞音乐专业的人都是一个全新的概念。在音乐领域中，计算机是一种新型的音乐表现形式，计算机音乐正逐渐改变着音乐届对音乐艺术的创作理念和思维习惯。以计算机为主的现代化多媒体教学已被广泛运用，本文通过对多媒体技术、优势、功能的说明，结合音乐课堂说明传统的音乐课教学方法与现代化多媒体技术相结合，不但有效地提高了音乐课的教学质量，而且可以大大提高学生对音乐课学习的兴趣，培养学生的创新能力，进一步推动素质教育的发展。     

分形音乐计算机生成技术研究

分形科学在经济、化工、计算机等各领域有着广泛的应用。在计算机科学方面，分形科学的应用有分形艺术图像的生成和图像分形压缩。而在音乐作曲领域，音乐学者对古典音乐的分析中，发现音乐也有分形性。既然音乐具有分形性，本文从计算机技术方面对分形音乐的自动生产做研究，探讨计算机分形音乐的算法。     

基于计算机图像处理的板形识别探析

近年来,随着我国科技水平得进一步提高,信息化技术得到较快的发展,计算机技术逐渐应用于我国的各个行业之中,其图像处理和板形识别技术在众多领域发挥了较大的作用,尤其是在医疗卫生领域、动漫领域、智能高科技领域等等。由此可见,计算机的图像处理的板形识别技术在我国计算机领域起着重要作用,这就需要更多的科研工作者加大力度进行深入地研究和开发,本篇文章主要针对该技术从应用范围、技术原理等角度进行了全方位的介绍和阐述,为我国计算机图像处理的板形识别问题提供了详细的理论依据。     

基于HMM的自动音符切分识别的研究

对于用户通过哼唱输入进行音乐检索系统中,音符的切分和识别是关键问题之一。本文介绍了利用隐马尔可夫模型对音符进行建模识别,完成用户哼唱输入自动音乐信息检索的前端处理的初步研究结果。文中给出了音乐中音符、静音及停顿模型的拓扑结构,通过规范化的训练建立了49个音符的隐马尔可夫参数模型。在音符的状态切分中,提出了基于k-均值聚类的状态粗切分方法,减少了手工劳作,提高了分割精度。研究结果表明;在没有语言模型的情况下,获得了46.04%的音符识别率,验证了其方法的可行性。本研究在音乐信息检索领域具有重要的意义。     

基于图像的OMR技术的实现

在分析OMR与OCR的应用特点后,提出了基于图像的OMR方式。详细讨论了该方法的硬件设计和工作原理,并在软件处理方面着重介绍了倾斜校正和图像分割。它具有对纸张质量要求低和识别准确度高等特点。     

分形音乐计算机生成技术研究

分形科学在经济、化工、计算机等各领域有着广泛的应用。在计算机科学方面,分形科学的应用有分形艺术图像的生成和图像分形压缩。而在音乐作曲领域,音乐学者对古典音乐的分析中,发现音乐也有分形性。既然音乐具有分形性,本文从计算机技术方面对分形音乐的自动生产做研究,探讨计算机分形音乐的算法。     

A music notation construction engine for optical music recognition

Optical music recognition (OMR) systems are used to convert music scanned from paper into a format suitable for playing or editing on a computer. These systems generally have two phases: recognizing the graphical symbols (such as note‐heads and lines) and determining the musical meaning and relationships of the symbols (such as the pitch and rhythm of the notes). In this paper we explore the second phase and give a two‐step approach that admits an economical representation of the parsing rules for the system. The approach is flexible and allows the system to be extended to new notations with little effort—the current system can parse common music notation, Sacred Harp notation and plainsong. It is based on a string grammar and a customizable graph that specifies relationships between musical objects. We observe that this graph can be related to printing as well as recognizing music notation, bringing the opportunity for cross‐fertilization between the two areas of research. Copyright © 2003 John Wiley & Sons, Ltd.     

简谱识别方法研究

简谱是大家非常熟悉和常用的乐谱之一,但是在目前光学乐谱识别领域中对它的研究几乎空白,研究的焦点都集中在五线谱识别上。在深入分析简谱特征的基础上,提出了一整套简谱识别的实现方法。输入的光学简谱经预处理后,首先通过每行的小节线特征提取出简谱部分,然后通过投影法和种子填充算法定位出简谱符号基元的位置,并由此采用不同的识别算法识别出每个简谱符号基元的类型,最后通过组装把各个简谱符号组装成音乐特征符,形成数字化乐谱。实验表明,这套方法对印刷乐谱的识别达到了令人满意的效果,是一项有意义的研究。     

Music symbol recognition by a LAG-based combination model

Most of optical music recognition (OMR) systems work under the assumption that the input image is scanner-based. However, we propose in this paper, camera based OMR system. Camera based OMR has a challengeable work in un-controlled environment such as a light, perspective, curved, transparency distortions and uneven staff-lines which tend to incur more frequently. In addition, the loss in performance of binarization methods, line thickness variation and space variation between lines are inevitable. In order to solve these problems, we propose a novel and effective staff-line removal method based on following three main ideas. First, a state-of-the-art staff-line detection method, Stable Path, is used to extract staff-line skeletons of the music score. Second, a line adjacency graph (LAG) model is exploited in a different manner over segmentation to cluster pixel runs generated from the run-length encoding (RLE) of an music score image. Third, a two-pass staff-line removal pipeline called filament filtering is applied to remove clusters lying on the staff-line. A music symbol is comprised of several parts so-called primitives, but the combination of these parts to form music symbol is unlimited. It causes difficulty applying the state-of-the-art method for music symbol recognition. To overcome these challenges and deal with primitive parts separately, we proposed a combination model which consists of LAG model, Graph model, and Set model as a framework for music symbol recognition. Our method shows impressive results on music score images captured from cameras, and gives high performance when applied to the ICDAR/GREC 2013 database, and a Gamera synthetic database. We have compared to some commercial software and proved the expediency and efficiency of the proposed method.     

A unified approach to content-based and fault-tolerant music recognition

In this paper, we propose a unified approach to fast index-based music recognition. As an important area within the field of music information retrieval (MIR), the goal of music recognition is, given a database of musical pieces and a query document, to locate all occurrences of that document within the database, up to certain possible errors. In particular, the identification of the query with regard to the database becomes possible. The approach presented in this paper is based on a general algorithmic framework for searching complex patterns of objects in large databases. We describe how this approach may be applied to two important music recognition tasks: The polyphonic (musical score-based) search in polyphonic score data and the identification of pulse-code modulation audio material from a given acoustic waveform. We give an overview on the various aspects of our technology including fault-tolerant search methods. Several areas of application are suggested. We describe several prototypic systems we have developed for those applications including the notify! and the audentify! systems for score- and waveform-based music recognition, respectively.     

Algorithmic compositions based on discovered musical patterns

Computer music composition is the dream of computer music researchers. In this paper, a top-down approach is investigated to discover the rules of musical composition from given music objects and to create a new music object of which style is similar to the given music objects based on the discovered composition rules. The proposed approach utilizes the data mining techniques in order to discover the styled rules of music composition characterized by music structures, melody styles and motifs. A new music object is generated based on the discovered rules. To measure the effectiveness of the proposed approach in computer music composition, a method similar to the Turing test was adopted to test the differences between the machine-generated and human-composed music. Experimental results show that it is hard to distinguish between them. The other experiment showed that the style of generated music is similar to that of the given music objects.     

音乐信息检索下的乐器识别综述

高效精准的乐器识别技术可以有效地推动声源分离、音乐识谱、音乐流派分类等研究的深入发展,可广泛应用于播放列表生成、声学环境分类、乐器智能教学和交互式多媒体等众多领域。近年来,随着乐器识别研究的不断推进,乐器识别系统在性能上有了大幅提高,但依旧存在着部分乐器难以识别、乐器音频特征提取较为困难、复音乐器识别精准度较低等诸多问题,如何借助人工智能技术对乐器进行高效精准的识别成为当前研究的热点和难点。针对当前研究现状,从乐器识别常用音频特征、乐器识别模型及方法和常用数据集三个方面进行综述,并对当前研究中存在的局限性和未来发展趋势进行总结,为乐器识别研究提供一定的借鉴参考。