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首页> 外文期刊>Audio, Speech, and Language Processing, IEEE/ACM Transactions on >Separation of Singing Voice Using Nonnegative Matrix Partial Co-Factorization for Singer Identification
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Separation of Singing Voice Using Nonnegative Matrix Partial Co-Factorization for Singer Identification

机译：使用非负矩阵部分协因子分离歌手识别的歌声

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摘要

In order to improve the performance of singer identification, we propose a system to separate singing voice from music accompaniment for monaural recordings. Our system consists of two key stages. The first stage exploits the nonnegative matrix partial co-factorization (NMPCF), which is a joint matrix decomposition integrating prior knowledge of singing voice and pure accompaniment to separate the mixture signal into singing voice portion and accompaniment portion. In the second stage, based on the separated singing voice obtained by the first stage, the pitches of singing voice are first estimated and then the harmonic components of singing voice can be distinguished. For a frame, the distinguished harmonic components are regarded as reliable while other frequency components unreliable, thus the spectrum is incomplete. With those harmonic components, the complete spectrums of singing voice can be reconstructed by a missing feature method, spectrum reconstruction, obtaining a refined signal with more clean singing voice. Experimental results demonstrate that, from the point view of source separation, the singing voice refinement can further improve in contrast with the singing voice separation using NMPCF, while for the point view of singer identification, the singing voice separated by NMPCF is more appropriate than the refined singing voice.
机译：为了提高歌手识别的性能，我们提出了一种将单声道录音中歌声与音乐伴奏分开的系统。我们的系统包括两个关键阶段。第一阶段利用非负矩阵部分共分解（NMPCF），这是一种结合了演唱声音和纯伴奏的先验知识的联合矩阵分解，以将混合信号分离为演唱声音部分和伴奏部分。在第二阶段，基于在第一阶段获得的分离的歌声，首先估计歌声的音调，然后可以区分歌声的谐波分量。对于一帧，可分辨的谐波分量被认为是可靠的，而其他频率分量则不可靠，因此频谱是不完整的。利用这些谐波分量，可以通过缺失特征方法（频谱重构）来重构歌声的完整频谱，从而获得具有更清晰歌声的精确信号。实验结果表明，从信号源分离的角度来看，与使用NMPCF分离歌声相比，唱歌声的细化可以进一步提高，而对于歌手识别的观点，由NMPCF分离出的歌声比通过NMPCF分离的歌声更合适。精致的歌声。

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