【作者】 杨波；

【导师】 阙大顺；

【作者基本信息】 武汉理工大学 ， 信号与信息处理， 2012， 硕士

【摘要】 如何从口益增长的多媒体资源中快速准确地定位已成为多媒体检索领域的研究热点,其相关技术的研究必将推动人机交互技术的发展,但目前基于内容的音频检索相对于基于内容的图像或视频检索来说发展滞后。哼唱检索是基于内容的音频检索的一个重要分支,是一种最自然、最方便的音乐检索方式,用户只需哼唱出一段旋律,就能在音乐数据库中检索出想要的歌曲。当前音乐检索支持的音乐格式主要是MIDI格式,而对MP3、WAV等格式的多音音乐检索的研究成果很不理想。本文在分析研究多音音乐检索的关键技术和难点问题的基础上,完成了哼唱输入的端点检测和目标音乐的音乐分割,改进并完善了基于基频矩阵的基音提取算法,归纳了用于音乐检索的DTW算法,实现了多音音乐的特征提取和特征匹配,最后设计实现了本文的多音音乐检索Music-Retrieval系统。本文主要研究的内容如下：(1)在分析音乐检索的原理和音乐乐理知识的基础上,选取了广泛用于音乐检索的旋律(音高、基音)特征来表征音乐;设计了基于内容的多音音乐检索的总体框架,分析了多音音乐检索的难点及本文研究实现的重点。(2)讨论了音乐信号的预处理,选用了基于频谱能量的端点检测算法实现了哼唱输入的端点检测,完成了对目标音乐的多音音乐分割,减少了后期特征提取和特征匹配的工作量。(3)仿真分析了基本的基于时域、频域的基音提取算法从含有复杂背景伴奏的目标多音音乐中提取基音特征的缺陷,在探讨现有的基于基频矩阵的基音提取算法的基础上,改进并完善了该算法,实现了在不能构造基频矩阵的情况下依然能够从目标音乐中提取到音乐的基音特征,并降低了计算的复杂度；(4)分析讨论了在语音识别领域中广泛使用的DTW算法用于多音音乐特征匹配的优缺点,归纳了适用于音乐检索的DTW算法,较好的克服了哼唱输入的音调误差,减少了匹配时间,实现了哼唱输入与目标音乐的特征匹配。(5)完成了本文Music-Retrieval系统的功能设计,采用VC++编程实现了该系统；采集了哼唱输入片段作为实验数据,完成了音乐检索系统的实验评测,包括算法的有效性实验,系统的检索性能、速度和精度等实验。更多还原

【Abstract】 How to find the required data from the growing multimedia resource accurately had become a research hotspot in the field of multimedia retrieval. The research of related technology will promote the development of Human-Computer Interaction. Compared with the Content-Based Image or Video Retrieval, Content-Based Audio Retrieval (CBAR) developed more backwardly. Query by Humming/Singing (QBHS), which is a branch of Content-Based Audio Retrieval, becomes one of the most natural and convenient music retrieval styles. When a user hums a piece of melody, music retrieval system can search the similar song from music database. Most existing music retrieval systems have been developed in the basis of MIDI files, instead of polyphonic music such as MP3or WAV files.On the basis of researching the key technologies and difficulties about the polyphonic music retrieval, the thesis completed the humming input endpoint detection and target music division, improved the existed pitch extraction algorithm based on fundamental frequency matrix, induced a dynamic time warping (DTW) algorithm fitting to music retrieval, realized the extraction and approximate matching of musical feature. At last, the thesis designed and implemented a polyphonic music retrieval system named Music-Retrieval.The main research contents of this thesis are as follows:(1) On the basis of probing the theory of music retrieval and the knowledge of music theory, this thesis chose the melody (pitch) that used in music retrieval widely as the expression of musical feature, designed the overall framework of the polyphonic music retrieval, as well as summarized the difficulties of the polyphonic music retrieval and the focus of this paper research.(2) This thesis discussed the knowledge of musical signal preprocessing, realized endpoint detection of the humming inputs, which choosing the algorithm based on energy-spectrum, and completed the segmentation of target music, which can decrease a large number of workload in the late stage of musical feature extraction and matching.(3) This thesis described the faults of some pitch extraction algorithms based on time or frequency domain. On the basis of studying the existed pitch extraction algorithm based on fundamental frequency matrix, this paper improved and perfected this algorithm, which can extract pitch form target music when having no ideal to construct the fundamental frequency matrix, and reduced the complexity of calculation.(4) This thesis analyzed and discussed the merits and drawbacks of DTW algorithm that used in voice recognition widely, induced a DTW algorithm fitting to music retrieval, completed musical features matching. The new algorithm can correct the tone errors of the humming inputs and decrease the time of calculation.(5) This thesis completed the functional design of the Music-Retrieval system, programmed and realized this system using VC++. At last, this paper collected a few humming inputs as experiment data, and completed experimental evaluation about Music-Retrieval system, including the validity of the algorithm, the performance, speed and accuracy of the system, etc.更多还原