NTT Communication Science Laboratories, NTT Corporation, Kanagawa, Japan.
Adv Exp Med Biol. 2013;787:519-26. doi: 10.1007/978-1-4614-1590-9_57.
Recent psychophysical and physiological studies demonstrated that auditory scene analysis (ASA) is inherently a dynamic process, suggesting that the system conducting ASA constantly changes itself, incorporating the dynamics of sound sources in the acoustic scene, to realize efficient and robust information processing. Here, we propose computational models of ASA based on two computational principles of ASA, namely, separation in a feature space and temporal regularity. We explicitly introduced learning processes, so that the system could autonomously develop its selectivity to features or bases for analyses according to the observed acoustic data. Simulation results demonstrated that the models were able to predict some essential features of behavioral properties of ASA, such as the buildup of streaming, multistable perception, and the segregation of repeated patterns embedded in distracting sounds.
最近的心理物理学和生理学研究表明，听觉场景分析（ASA）本质上是一个动态的过程，这表明进行 ASA 的系统不断地改变自身，将声学场景中声源的动态纳入其中，以实现高效和强大的信息处理。在这里，我们提出了基于 ASA 的两个计算原则，即特征空间分离和时间规律性的 ASA 计算模型。我们明确引入了学习过程，以便系统能够根据观察到的声学数据自主地发展其对特征或分析基础的选择性。模拟结果表明，这些模型能够预测 ASA 的行为特性的一些基本特征，例如流的建立、多稳定感知以及分散在干扰声音中的重复模式的分离。
