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中文摘要：
针对基本反复模型音乐分离方法自适应性差的问题，提出一种基于美标度倒谱系数（MFCC）的多反复结构模型的音乐分离方法。首先，提取出音乐信号的MFCC系数矩阵（39维的数据构成）；然后利用余弦特性得到其相似矩阵，进而将相似度一致的片段划分到一起，建立不同的反复结构模型；之后结合理想二元掩蔽（IBM）分离出背景音乐及歌声的频谱，相应的时域信号则由傅里叶逆变换获得；最后，在不同类型、长度的音乐文件上测试了算法性能，将提出的算法与Rafii的反复算法和Ozerov的灵活窗非负矩阵分解方法进行对比。实验结果表明，改进方法在分离性能上最高提高3dB左右，并且对于曲调变换大的音乐提高效果更为明显，从而证实了改进方法是一种有效的音乐分离方法，并且更具稳定性。
英文摘要：
For the poor adaptability of the original repeating pattern, an improved music separation method of multirepeating structure of Mel-Frequency Cepstrum Coefficient （MFCC） was proposed. Firstly, the MFCC coefficient matrix （39-dimensional data） of the music signal was extracted; then the cosine characteristic was applied to the count of similarity matrix of MFCC, and putted the fragments with consistent similarity together, next built different repeating patterns for groups with different, thereby the spectrums of the background music and vocal were separated combined with ideal binary masking （IBM）, the corresponding time domain signals were obtained by inverse Fourier transform; finally, the improved method was tested on the music database of different types and length, and the separation results were compared with repeating method of Rafii and the non-negative matrix factorization based on flexible framework method of Ozerov. The experimental results showed that the separation performance of improved method was improved about 3 dB, the performance of music with melody changed larger was significantly improved, thus verifying that that the improved method was an effective music separation algorithm and more
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