Most sound scenes result from the superposition of several sources, which can be separately perceived and analyzed by human listeners. Source separation aims to provide machine listeners with similar skills by extracting the sounds of individual sources from a given scene. Existing separation systems operate either by emulating the human auditory system or by inferring the parameters of probabilistic sound models. In this chapter, we focus on the latter approach and provide a joint overview of established and recent models, including independent component analysis, local time-frequency models and spectral template-based models. We show that most models are instances of one of the following two general paradigms: linear modeling or variance modeling. We compare the merits of either paradigm and report objective performance figures. We conclude by discussing promising combinations of probabilistic priors and inference algorithms that could form the basis of future state-of-the-art systems.

We examine the problem of blind audio source separation using Independent Component Analysis (ICA). In order to separate audio sources recorded in a real recording environment, we need to model the mixing process as convolutional. Many methods have been introduced for separating convolved mixtures, the most successful of which require working in the frequency domain [l], [2], [3], [4]. This paper proposes a fixed-point algorithm for performing fast frequency domain ICA, as well as a method to increase the stability and enhance the performance of previous frequency domain ICA algorithms.

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Highlights Treatment of T-47D breast cancer cells with silvestrol sensitised them to radiation. 1 nM silvestrol caused a 34% reduction in cells exposed to 2 Gy. Clonogenic assays revealed silvestrol had a dose modifying factor of 1.4. Radiation was delivered to the tissue culture plate using a clinical LINAC machine.