A bio-inspired geometric model for sound reconstruction.

IF 2.3 4区 医学 Q1 Neuroscience Journal of Mathematical Neuroscience Pub Date : 2021-01-04 DOI:10.1186/s13408-020-00099-4
Ugo Boscain, Dario Prandi, Ludovic Sacchelli, Giuseppina Turco
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引用次数: 1

Abstract

The reconstruction mechanisms built by the human auditory system during sound reconstruction are still a matter of debate. The purpose of this study is to propose a mathematical model of sound reconstruction based on the functional architecture of the auditory cortex (A1). The model is inspired by the geometrical modelling of vision, which has undergone a great development in the last ten years. There are, however, fundamental dissimilarities, due to the different role played by time and the different group of symmetries. The algorithm transforms the degraded sound in an 'image' in the time-frequency domain via a short-time Fourier transform. Such an image is then lifted to the Heisenberg group and is reconstructed via a Wilson-Cowan integro-differential equation. Preliminary numerical experiments are provided, showing the good reconstruction properties of the algorithm on synthetic sounds concentrated around two frequencies.

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仿生几何模型用于声音重建。
人类听觉系统在声音重建过程中建立的重建机制仍然是一个有争议的问题。本研究的目的是提出一个基于听觉皮层(A1)功能结构的声音重建数学模型。该模型的灵感来源于近十年来有了很大发展的视觉几何建模。然而,由于时间所扮演的不同角色和不同组的对称性,存在着根本的不同。该算法通过短时傅里叶变换在时频域对“图像”中的退化声音进行变换。这样的图像然后被提升到海森堡群,并通过威尔逊-考恩积分-微分方程重建。初步的数值实验表明,该算法对集中在两个频率附近的合成声音具有良好的重构性能。
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来源期刊
Journal of Mathematical Neuroscience
Journal of Mathematical Neuroscience Neuroscience-Neuroscience (miscellaneous)
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审稿时长
13 weeks
期刊介绍: The Journal of Mathematical Neuroscience (JMN) publishes research articles on the mathematical modeling and analysis of all areas of neuroscience, i.e., the study of the nervous system and its dysfunctions. The focus is on using mathematics as the primary tool for elucidating the fundamental mechanisms responsible for experimentally observed behaviours in neuroscience at all relevant scales, from the molecular world to that of cognition. The aim is to publish work that uses advanced mathematical techniques to illuminate these questions. It publishes full length original papers, rapid communications and review articles. Papers that combine theoretical results supported by convincing numerical experiments are especially encouraged. Papers that introduce and help develop those new pieces of mathematical theory which are likely to be relevant to future studies of the nervous system in general and the human brain in particular are also welcome.
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