鲁棒性哼唱特征研究
发布时间：2018-08-13 09:12
【摘要】：哼唱检索系统,一种基于内容的多媒体检索系统,作为目前的研究热点,一直存在哼唱特征不稳定的问题,造成此问题的主要原因是人声个性化,哼唱人的音域不同、哼唱节奏不同、个别音高不准确等问题,都会导致哼唱特征不稳定。本文针对哼唱检索系统中存在的哼唱特征不稳定问题,进行了以下几点研究,旨在提取更具鲁棒性的哼唱特征:1、哼唱片段特征提取改进算法针对哼唱音频,提取哼唱音高特征后,为提高哼唱片段与MIDI的匹配度,改进了哼唱片段的特征提取算法。通过分析人声哼唱频率分布范围,哼唱与MIDI的对齐实验,对哼唱音高进行规整操作;结合乐理知识,进行音符切分操作;根据人声个性化进行半音域转换,以哼唱人的基准频率代替原440Hz的统一基准频率,使得哼唱音符特征与midi的音符特征值达到更好的匹配。以上改进方法通过实验给出算法的有效性。2、提出了一种基于局部统计的哼唱特征提取算法通过对哼唱音符序列在纵向音域分布和横向时序变化的局部统计,获得哼唱旋律的直方图统计特征。此算法在纵向音域上进行区间分布投影统计;在横向上进行音符时序变化模式直方图分布统计,最终获得纵向与横向的联合直方图特征,并加入均值、极差、方差特征。最后以4段连续子序列加整段联合直方图特征对音符分布进行描述。此算法不同于传统的以音高或音符直接作为哼唱特征的表示方法,而是将音符转化为统计特征的形式,保证了哼唱特征的相对稳定,对于不同用户在哼唱中表现的速度、音域、节奏等方面的差异有很好的容错性。最后,通过实验检验了此方法的有效性,实验数据为5000首MIDI,104首哼唱查询,应用本文提出的哼唱特征提取算法作为哼唱特征,并采用局部敏感哈希算法(Local Sensitive Hash)作为相似特征匹配算法,得到TOP1准确率为86%,TOP5准确率为92%,与原哼唱识别系统中以音符作为特征的结果进行了比较,实验结果优于原始结果。
[Abstract]:Hem retrieval system, a content-based multimedia retrieval system, has been a hot research topic at present. The main cause of this problem is the personalization of human voice and the different phonetic range of humming person. Hem rhythm is different, individual pitch inaccurate and other problems, will lead to the characteristics of humming instability. In this paper, aiming at the instability of humming feature in humming retrieval system, the following researches are carried out to extract the more robust humming feature: 1, and the improved feature extraction algorithm for humming audio. In order to improve the matching degree between humming segment and MIDI, the feature extraction algorithm of humming segment is improved. By analyzing the range of frequency distribution of human humming, the alignment experiment between humming and MIDI, the tonal pitch of humming is regularized; combining with the knowledge of music theory, the notes are segmented, and the semitone conversion is carried out according to the personality of human voice. The standard frequency of humming person is replaced by the unified reference frequency of the original 440Hz, which makes the feature of humming note and the characteristic value of note of midi better match. The above improved method gives the effectiveness of the algorithm through experiments. 2. A local statistics based humming feature extraction algorithm is proposed. The local statistics of the distribution of humming notes in the longitudinal range and the change of horizontal time series are presented. The histogram statistical features of humming melody are obtained. In this algorithm, interval distribution projection statistics are performed on the longitudinal range, and the histogram distribution statistics of the pattern of note timing change are carried out horizontally. Finally, the joint histogram features of longitudinal and horizontal are obtained, and the features of mean value, range difference and variance are added. Finally, the note distribution is described by four successive subsequences plus the whole segment combined histogram feature. This algorithm is different from the traditional representation method in which pitch or note is directly used as the feature of humming, but it transforms the note into the form of statistical feature, which ensures the relative stability of the feature of humming. Rhythm and other aspects of the difference has a good fault-tolerance. Finally, the validity of this method is verified by experiments. The experimental data is 5000 midi 104s humming query, and the Hem feature extraction algorithm proposed in this paper is used as the humming feature. The local sensitive hashing algorithm (Local Sensitive Hash) is used as the similar feature matching algorithm, and the accuracy of TOP1 is 86 and TOP5 is 922.It is compared with the results of the original humming recognition system with the notes as the feature. The experimental results are better than the original ones.
【学位授予单位】：北京邮电大学
【学位级别】：硕士
【学位授予年份】：2016
【分类号】：TN912.3;TP391.3
本文编号：2180532
[Abstract]:Hem retrieval system, a content-based multimedia retrieval system, has been a hot research topic at present. The main cause of this problem is the personalization of human voice and the different phonetic range of humming person. Hem rhythm is different, individual pitch inaccurate and other problems, will lead to the characteristics of humming instability. In this paper, aiming at the instability of humming feature in humming retrieval system, the following researches are carried out to extract the more robust humming feature: 1, and the improved feature extraction algorithm for humming audio. In order to improve the matching degree between humming segment and MIDI, the feature extraction algorithm of humming segment is improved. By analyzing the range of frequency distribution of human humming, the alignment experiment between humming and MIDI, the tonal pitch of humming is regularized; combining with the knowledge of music theory, the notes are segmented, and the semitone conversion is carried out according to the personality of human voice. The standard frequency of humming person is replaced by the unified reference frequency of the original 440Hz, which makes the feature of humming note and the characteristic value of note of midi better match. The above improved method gives the effectiveness of the algorithm through experiments. 2. A local statistics based humming feature extraction algorithm is proposed. The local statistics of the distribution of humming notes in the longitudinal range and the change of horizontal time series are presented. The histogram statistical features of humming melody are obtained. In this algorithm, interval distribution projection statistics are performed on the longitudinal range, and the histogram distribution statistics of the pattern of note timing change are carried out horizontally. Finally, the joint histogram features of longitudinal and horizontal are obtained, and the features of mean value, range difference and variance are added. Finally, the note distribution is described by four successive subsequences plus the whole segment combined histogram feature. This algorithm is different from the traditional representation method in which pitch or note is directly used as the feature of humming, but it transforms the note into the form of statistical feature, which ensures the relative stability of the feature of humming. Rhythm and other aspects of the difference has a good fault-tolerance. Finally, the validity of this method is verified by experiments. The experimental data is 5000 midi 104s humming query, and the Hem feature extraction algorithm proposed in this paper is used as the humming feature. The local sensitive hashing algorithm (Local Sensitive Hash) is used as the similar feature matching algorithm, and the accuracy of TOP1 is 86 and TOP5 is 922.It is compared with the results of the original humming recognition system with the notes as the feature. The experimental results are better than the original ones.
【学位授予单位】：北京邮电大学
【学位级别】：硕士
【学位授予年份】：2016
【分类号】：TN912.3;TP391.3
【参考文献】
相关期刊论文 前4条
1 王恩成;苏腾芳;袁开国;伍淳华;王玉庆;;哼唱检索中联合音高与能量的音符切分算法[J];计算机工程;2012年09期
2 毛大伟;曹华;木拉提.哈米提;童勤业;;基于美尔倒谱系数和复杂性的说话人识别[J];生物医学工程学杂志;2006年04期
3 刘建;郑方;吴文虎;;基于幅度差平方和函数的基音周期提取算法[J];清华大学学报(自然科学版);2006年01期
4 徐险峰;基于内容的多媒体信息检索技术[J];现代情报;2005年03期
，本文编号：2180532
本文链接：https://www.wllwen.com/kejilunwen/ruanjiangongchenglunwen/2180532.html
