Abstract In this paper, we introduce time-domain and frequency-domain versions of a new Blind Source Separation (BSS) approach to extract bounded magnitude sparse sources from convolutive mixtures. We derive algorithms by maximization of the proposed objective functions that are defined in a completely deterministic framework, and prove that global maximums of the objective functions yield perfect separation under suitable conditions. The derived algorithms can be applied to temporal or spatially dependent sources as well as independent sources. We provide experimental results to demonstrate some benefits of the approach, also including an application on blind speech separation.

中文翻译：

---

卷积混合的基于时间和频率的稀疏有界分量分析算法

摘要 在本文中，我们介绍了一种新的盲源分离 (BSS) 方法的时域和频域版本，以从卷积混合中提取有界幅度稀疏源。我们通过最大化在完全确定性框架中定义的目标函数来推导算法，并证明目标函数的全局最大值在合适的条件下产生完美的分离。导出的算法可以应用于时间或空间相关源以及独立源。我们提供了实验结果来证明该方法的一些好处，还包括盲语音分离的应用。