基于时频掩蔽的BSS确定及其在谐波矢量分析中的应用
Determined BSS based on time-frequency masking and its application to harmonic vector analysis
摘要：
本文提出了一种基于谐波矢量分析的音频盲源分离算法框架。当麦克风和源的数量相等时（确定的情况），用于卷积音频混合的BSS通常通过多通道线性滤波来执行。本文解决了这种确定BSS的批处理的基础上。为了估计解混滤波器，源信号的有效建模是重要的。一个成功的例子是独立向量分析（IVA），它通过每个源中频率分量之间的共现来对信号进行建模。为了给源建模更多的自由度，本文提出了一个确定BSS的一般框架。它是基于即插即用方案，使用原始-对偶分裂算法，使我们能够通过时间-频率掩模隐式地对源信号进行建模。通过使用所提出的框架，确定BSS算法可以通过设计掩模，增强源信号。作为其应用的一个例子，我们通过定义一个时频掩模来提出HVA，该掩模通过倒谱的稀疏性增强音频信号的谐波结构。实验表明，HVA优于IVA和独立低秩矩阵分析（ILRMA）的语音和音乐信号。本文提供了MATLAB代码供参考（https://doi.org/10.24433/CO.9507820.v1）。
英文摘要：
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 ).
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
