一种基于隐马尔科夫模型的波形文件主旋律基频提取算法

哼唱检索中通常以旋律的基频作为音乐特征进行检索,目前研究的哼唱检索系统都是基于MIDI音乐文件。但是,目前存在的MIDI音乐文件的数量非常少,基于波形文件的哼唱检索系统才是未来的发展趋势。本文针对单声道波形文件,研究了一种提取歌曲主旋律基频曲线的算法。该算法将隐马尔科夫模型和“谐波乐器/打击乐器声音分离”模型进行结合。实验表明该算法对主旋律基频提取具有很高的准确率。     

基于挖掘技术的音乐风格识别系统

目前使用计算机来识别音乐的风格的实践活动是相对比较少的。利用音乐的旋律特征可以帮助计算机识别音乐的风格，数据挖掘技术是开展这项工作的一条途径。这方面的工作涉及了音乐旋律的表达、旋律特征的抽取以及分类技术等许多内容。介绍了一个音乐风格识别系统MSC，系统以MIDI乐曲为数据源提取出乐曲的旋律，对不同风格乐曲的旋律进行了频繁模式的挖掘和对测试乐曲的风格识别。     

Finding maximum-length repeating patterns in music databases

This paper introduces the problem of discovering maximum-length repeating patterns in music objects. A novel algorithm is presented for the extraction of this kind of patterns from a melody music object. The proposed algorithm discovers all maximum-length repeating patterns using an “aggressive” accession during searching, by avoiding costly repetition frequency calculation and by examining as few as possible repeating patterns in order to reach the maximum-length repeating pattern(s). Detailed experimental results illustrate the significant performance gains due to the proposed algorithm, compared to an existing baseline algorithm.

基于音高旋律元的柔和乐曲的自动作曲算法

提出了一种以柔和为导向、以音高旋律元 (Pitch melody unit, PMU)为基本操作单位的自动作曲算法.首先根据柔和乐曲的旋律特征构建了算法的 规则模型.然后定义了一阶音高旋律元和二阶音高旋律元,构建了音高旋律元转换表 (Pitch melody unit transition tables, PMUTTs)自动生成的模型,以表达相邻音高旋律元的链接概率,为 旋律计算提供基本数据.分别定义了用来自动生成新的音高旋律元、乐节、乐句、乐曲的4个旋律计算的算子,以自动生成乐曲.实验结果表明, 与以音符为基本单位的马尔可夫作曲方法相比,以音高旋律元为基本单位进行作曲可以提高旋律的逻辑严密性和美感度.     

基于序列模型的音乐词曲匹配度智能评估算法

情感匹配模型是一种常用于评价词曲匹配程度的方法;然而,单纯地依靠情感匹配模型无法对评价词曲匹配度进行准确的评价。为改善此问题,提出了基于序列模型的词曲匹配度智能评估算法,其综合考虑词曲情感和词曲间节奏关系以给出一个更加准确的词曲评估方法。基于公开词曲同步数据集,构建了音乐情感和节奏正反例模型,并基于此模型将音乐切分成片段;进一步,将歌词和旋律片段分别通过歌词编码器和旋律编码器进行编码,并将编码后具有上下语境的歌词特征和旋律特征输入词曲匹配解码器,解析词曲间特征关系,判断词曲片段匹配程度。仿真结果表明:基于序列模型的词曲匹配度智能评估算法,相对于单纯的情感匹配模型,能够更精确地评价词曲匹配程度,验证了本文提出算法的有效性。     

基于音乐旋律轮廓的特征提取算法

提出了一种基于音乐旋律轮廓的特征提取算法。该算法从哼唱片断中提取出歌曲基音序列,经规整、合并、分段后转化为旋律轮廓序列,然后使用标准音调生成的标准音调差值表将此序列转化为旋律轮廓特征。结果表明,该系统对环境噪声有较好的鲁棒性;在含有405首歌曲的搜索空间中,检索前5位成功率超过90%。     

利用互信息实现音乐风格的分类

提出了一种基于相对音高和相对时延表达音乐旋律的模型,这种模型能够更好地反映不同演奏方式下的同一旋律型。基于上述表达模型提出的音乐风格分类算法通过旋律互信息度量音乐风格,同Unigram和Bigram模型的切分算法具有相近的时间复杂度,能够更好地支持具有多种音乐风格的乐曲分类。     

Pattern discovery and change detection of online music query streams

Mining of music data is one of the most important problems in multimedia data mining. In this paper, two research issues of mining music data, i.e., online mining of music query streams and change detection of music query streams, are discussed. First, we proposed an efficient online algorithm, FTP-stream (Frequent Temporal Pattern mining of streams), to mine all frequent melody structures over sliding windows of music melody sequence streams. An effective bit-sequence representation is used in the proposed algorithm to reduce the time and memory needed to slide the windows. An effective list structure is developed in the FTP-stream algorithm to overcome the performance bottleneck of 2-candidate generation. Experiments show that the proposed algorithm FTP-stream only needs a half of memory requirement of original melody sequence data, and just scans the music query stream once. After mining frequent melody structures, we developed a simple online algorithm, MQS-change (changes of Music Query Streams), to detect the changes of frequent melody structures in current user-centered music query streams. Two music melody structures (set of chord-sets and string of chord-sets) are maintained and four melody structure changes (positive burst, negative burst, increasing change and decreasing change) are monitored in a new summary data structure, MSC-list (a list of Music Structure Changes). Experiments show that the MQS-change algorithm is an effective online method to detect the changes of music melody structures over continuous music query streams.

流行音乐主旋律提取技术综述

旋律是最重要的音乐要素之一,多应用于音乐内容分析、音乐创作、音乐教育、抄袭检测等方面。主旋律提取旨在从一段音乐中自动估计对应于主旋律单音音符序列的音高或基频。流行音乐一般属于复杂的多音音乐,因此主旋律提取面临着许多挑战。综述了主旋律提取的研究背景,将主旋律提取的典型方法分别从基于音高显著度、基于声源分离以及其他方法3个类别进行了阐述,最后介绍了主旋律提取的评价指标以及研究进展。     

基于变分自编码器的交互式旋律生成方法

现有的交互式神经音乐生成方法主要存在控制模式不灵活、数据标注困难以及模型难以优化等问题。针对这些问题,提出了一种基于变分自编码器(VAE)的无监督交互式旋律生成方法。通过为VAE引入显式的旋律轮廓条件推理学习,实现了对生成旋律局部与全局特征的灵活控制。实验表明,该方法易于优化且具有良好的旋律局部与全局特征的控制能力。通过对大量生成样本的分析,证明了模型从音乐数据中学习到了有用的音乐知识。     

MEMSA: mining emerging melody structures from music query data

Effective and efficient mining of music structure patterns from music query data is one of the most interesting issues of multimedia data mining. In this paper, we introduce a new kind of pattern, called emerging melody structure (EMS), for knowledge discovery from music melody streams. EMSs are defined as music data items with melody strings whose support increase significantly from one sliding window to another window from streaming melody sequences. The discovered EMS can be used to predict the future trend of online music style recommendation, to personalize the Web service of music downloading priority, for music composers to compose new music or for service provider to collect more similar music. Therefore, an efficient data mining approach, called MEMSA (Mining Emerging Melody Structure Algorithm), is proposed to discover all EMSs from streaming music query data over sliding windows. In the framework of MEMSA, a prefix tree-based data structure, called EMS-tree (Emerging Melody Structure tree), is constructed for maintaining temporal EMSs effectively. Experimental results show that the proposed method MEMSA is an efficient algorithm for mining all EMSs from streaming melody sequences efficiently.     

一种新的近似旋律匹配方法及其在哼唱检索系统中的应用

提出了一种近似旋律匹配(approximate meltlody matching)的新方法——线性对齐匹配法,并在此基础上实现了一个哼唱检索(queryby humming)系统原型．与已有的基于内容的音乐检索(content-based music retrieval)不同,该算法并非基于近似符号串匹配、统计模型或者特征空间,而是根据相近旋律的音高轮廓在几何上的相似性,将音高和节奏特征一并考虑所设计而成的全新算法．通过实验检验该算法的有效性,在含有3864首乐曲的搜索空间中,检索62段人声哼唱,线性对齐匹配法取得了90．3％的前3位命中率,相比传统的近似符号匹配算法高出11％以上．这一实验结果有力地表明了线性对齐匹配法的有效性,及其应用于大型数字音乐检索引擎的可行性．     

True suffix tree approach for discovering non-trivial repeating patterns in a music object

As the growing in Internet, database types and sizes are getting various and larger. The topic of finding out the significant information from a database at the shortest time is important. In the music databases, a repeating pattern is an important feature of music objects, which commonly used in analyzing the repeated part of music data and looking for themes. Most of the repeating patterns are key melodies or easy to familiarize and remember for people. Therefore, we can use the themes or the repeating patterns to construct indices that can speedup query execution for music retrievals. Nevertheless, non-trivial repeating patterns exclude those patterns, which are all contained in other longer patterns, such that they can reduce the redundancy of the repeating patterns and save the index space needed. Most of existing algorithms are time consuming for finding non-trivial repeating patterns in a music object. In this research, we aim to apply the true suffix tree approach to discover non-trivial repeating patterns for a music object, which can efficiently address the cost problems in processing time and memory space. In general case, our proposed scheme can extract non-trivial repeating patterns in a linear time.

基于音轨特征量的多音轨MIDI主旋律抽取方法

在基于内容的数字音乐检索研究中,其音乐库大都直接使用复合音音乐数据文件。然而这种直接采用复合音音乐数据的索引必将给检索处理带来巨大的计算量以及复杂的匹配算法。该文提出了一种基于音轨特征量的多音轨MIDI主旋律信息音轨抽取方法。通过与人工标注结果的实验比较,表明该文实现的抽取方法可有效地从多音轨MIDI演奏数据文件中提取出主旋律音轨。     

一种改进的哼唱式音乐检索方法

针对哼唱式音乐检索的非准确性,本文提出了一种新的旋律表示方法,并根据旋律的表示采用了两次匹配算法,将相似度最高的乐曲输出。实验结果表明,该方法提高了哼唱式音乐检索的准确率。     

旋律提取技术研究综述

旋律提取技术是基于内容的音乐信息检索的关键技术之一。对国内外旋律提取技术的已有研究工作进行了分类介绍,分别总结归纳了针对符号表示的音乐文件与针对波形表示的音乐文件的旋律提取算法,分析了各种算法的特点,阐明了该方向的未来发展趋势。     

计算机音乐制作与数字音频

近年来,随着计算机的快速发展,利用计算机的音乐制作以及数字音频的制作得到了很广泛的应用。分析了现代音乐的创作过程,通过计算机的智能算法作曲功能,详细解释了不同计算机语言在识别音乐时的相关理论基础。同时,在计算机的辅助功能下,让音乐的旋律实现了音乐的二维可视化。最后,根据音乐旋律中的响度特征实现了音乐的自动识别,分析了音乐旋律与计算机的关系,为后续的学者分析提供了相应的参考。     

基于音乐情感识别的舞台灯光控制方法研究

为了实现音乐情感识别的舞台灯光自动控制,需对音乐文件进行情感标记。针对人工情感标记效率低、速度慢的问题,开展了基于音乐情感识别的舞台灯光控制方法研究,提出了一种基于支持向量机和粒子群优化的音乐情感特征提取、分类和识别算法。首先以231首MIDI音乐文件为例,对平均音高、平均音强、旋律的方向等7种音乐基本特征进行提取并进行标准化处理;之后组成音乐情感特征向量输入支持向量机（SVM）多分类器,并利用改进的粒子群算法（PSO）优化分类器参数,建立标准音乐分类模型;最后设计灯光动作模型,将新的音乐文件通过离散情感模型与灯光动作相匹配,生成舞台灯光控制方法。实验结果表明了情感识别模型的有效性,与传统SVM多分类模型相比,明显提高了音乐情感的识别率,减少了测试时间,从而为舞台灯光设计人员提供合理参考。     

Music snippet extraction via melody-based repeated pattern discovery

In this paper, we present a complete set of procedures to automatically extract a music snippet, defined as the most representative or the highlighted excerpt of a music clip. We first generate a modified and compact similarity matrix based on selected features and distance metrics, and then several improved techniques for music repeated pattern discovery are utilized because a music snippet is usually a part of the repeated melody, main theme or chorus. During the process, redundant and wrongly detected patterns are discarded, boundaries are corrected using beat information, and final clusters are also further sorted according to the occurrence frequency and energy information. Subsequently, following our methods, we designed a music snippet extraction system which allows users to detect snippets. Experiments performed on the system show the superiority of our proposed approach. Supported by the National Natural Science Foundation of China (Grant No. 60873098)     

旋律弹性匹配实现Web哼唱检索

面对日益增加的音乐作品及多样化的用户检索需求,采用基于内容的音乐检索方式,提出一种对音乐旋律的定义及表达方式,并在此基础上依照旋律轮廓的几何相似性实现音乐的相似度计算,具体地提出影响检索算法准确度的若干因素,通过实验的方法确定相应的阈值,最后实现检索系统的单机版开发和Web版发布。实验表明,所采用的方法正确、有效,能够取得较高的检索准确度。