论文标题
基于时频掩蔽及其应用于谐波矢量分析的确定BSS
Determined BSS based on time-frequency masking and its application to harmonic vector analysis
论文作者
论文摘要
本文提出了基于音频盲源分离（BSS）的一般算法框架的谐波矢量分析（HVA）。当麦克风和源的数量相等时，通常通过多通道线性滤波来进行复杂的音频混合物的BSS。本文根据批处理处理解决了此类确定的BSS。为了估计解散过滤器，对源信号的有效建模很重要。一个成功的示例是独立的矢量分析（IVA），该分析是通过每个源中频率分量之间共同发生建模信号的。为了使源建模更加自由，本文提出了确定的BSS的一般框架。它基于使用Primal Dual分裂算法的插件播放方案，使我们能够通过时间频面掩码隐式对源信号进行建模。通过使用所提出的框架，可以通过设计增强源信号的面具来开发确定的BSS算法。作为其应用程序的一个例子，我们通过定义一个时频掩模提出HVA，该屏蔽通过Cepstrum的稀疏性增强音频信号的谐波结构。实验表明，对于语音和音乐信号，HVA的表现优于IVA和独立的低级矩阵分析（ILRMA）。提供了MATLAB代码以及论文以供参考（https://doi.org/10.24433/co.9507820.v1）。
This paper proposes harmonic vector analysis (HVA) based on a general algorithmic framework of audio blind source separation (BSS) that is also presented in this paper. BSS for a convolutive audio mixture is usually performed by multichannel linear filtering when the numbers of microphones and sources are equal (determined situation). This paper addresses such determined BSS based on batch processing. To estimate the demixing filters, effective modeling of the source signals is important. One successful example is independent vector analysis (IVA) that models the signals via co-occurrence among the frequency components in each source. To give more freedom to the source modeling, a general framework of determined BSS is presented in this paper. It is based on the plug-and-play scheme using a primal-dual splitting algorithm and enables us to model the source signals implicitly through a time-frequency mask. By using the proposed framework, determined BSS algorithms can be developed by designing masks that enhance the source signals. As an example of its application, we propose HVA by defining a time-frequency mask that enhances the harmonic structure of audio signals via sparsity of cepstrum. The experiments showed that HVA outperforms IVA and independent low-rank matrix analysis (ILRMA) for both speech and music signals. A MATLAB code is provided along with the paper for a reference ( https://doi.org/10.24433/CO.9507820.v1 ).
